# Are dynamic tool lists allowed when using create\_agent?

**URL:** <https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920>\
**Category:** Talking Shop\
**Tags:** intro-to-langgraph, python-help\
**Created:** [October 24, 2025, 9:59am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920 "2025-10-24T09:59:57Z")\
**Posts on this page:** 17\
**Page:** 1

<div class="post-metadata">

**Author:** ![Batiste](https://avatars.discourse-cdn.com/v4/letter/b/a88e4f/32.png) [@Batiste](https://forum.langchain.com/u/Batiste)\
**Post date:** [October 24, 2025, 9:59am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/1 "2025-10-24T09:59:57Z")

</div>

Hi,

I’m trying to make create\_agent work a bit like Claude Skills, where the agent can discover tools when loading skill\_tools (which are basically a long string with instructions + add new tools to its list). But it seems like I need to define the tools list right from the start.

```auto
skills_tools = [skill_search, skill_math]

agent = create_agent(tools = skills_tools)

@tool
def skill_search():
  """Loads the context you need to search well"""
  
  # This part I don't know how to code ^^
  parent.tool_list += [fast_search, rag, deep_search]

  # This is clear
  instructions = """long text explaining how to search"""
  return instructions

```

Is that the case ? Any tips to make it work ?

Thanks

---

<div class="post-metadata">

**Author:** ![mcavdar](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/mcavdar/32/720_2.png) [@mcavdar](https://forum.langchain.com/u/mcavdar)\
**Post date:** [October 24, 2025, 10:40am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/2 "2025-10-24T10:40:54Z")

</div>

hi @Batiste

I think you need something like this:

> **[What's new in v1 - Docs by LangChain](https://docs.langchain.com/oss/python/releases/langchain-v1#custom-middleware)**

BTW, the example is buggy — you should take these issues/PRs into account:

> <https://github.com/langchain-ai/docs/issues/1113>
>
> \### Type of issue
> 
> issue / bug
> 
> \### Language
> 
> Python
> 
> \### Description
> 
> \`\`\`
> from …langchain.agents.middleware import (
> AgentMiddleware,
> ModelRequest,
> ModelRequestHandler
> )
> \`\`\`
> 
> Running the imports from \[this page\](https://docs.langchain.com/oss/python/releases/langchain-v1#custom-middleware) results in the following error:
> \`ImportError: cannot import name 'ModelRequestHandler' from 'langchain.agents.middleware'\`
> 
> I’ll update the examples soon based on the new documentation at: https://docs.langchain.com/oss/python/langchain/middleware#custom-middleware.

and

> <https://github.com/langchain-ai/docs/pull/1104/files>
>
> \## Overview
> Fixes a small typo in the LangChain documentation where \`Middleware…\` was used instead of \`AgentMiddleware\` in an example for \`ExpertiseBasedToolMiddleware\`.
> 
> \## Type of change
> \*\*Type:\*\* Fix typo/bug/link/formatting
> 
> \## Related issues/PRs
> \- None
> 
> \## Checklist
> \- \[x\] I have read the \[contributing guidelines\](README.md)
> \- \[x\] I have verified the class name in the current LangChain API
> \- \[x\] All code examples have been tested and work correctly
> \- \[x\] Root relative paths verified
> \- \[x\] N/A — no navigation updates needed
> \- \[x\] I have gotten approval from the relevant reviewers
> 
> \## Additional notes
> This change aligns the example with the current LangChain API (\`AgentMiddleware\` is the correct base class).

It should look something like this:

`request: ModelRequest,`  
`handler: Callable[[ModelRequest], ModelResponse],`  
`) → ModelResponse:`

Also, make sure to import `ModelResponse` from `langchain.agents.middleware.types`

---

<div class="post-metadata">

**Author:** ![Batiste](https://avatars.discourse-cdn.com/v4/letter/b/a88e4f/32.png) [@Batiste](https://forum.langchain.com/u/Batiste)\
**Post date:** [October 24, 2025, 11:05am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/3 "2025-10-24T11:05:24Z")

</div>

I received the following tips from a AI doc search. Will try this afternoon.

```auto
from langchain.agents import create_agent  
from langchain.agents.middleware import AgentMiddleware  
from langchain_core.tools import tool  
  
# Define ALL possible tools upfront  
@tool  
def fast_search(query: str) -> str:  
    """Fast search tool"""  
    return f"Fast results for: {query}"  
  
@tool  
def rag(query: str) -> str:  
    """RAG search tool"""  
    return f"RAG results for: {query}"  
  
@tool  
def deep_search(query: str) -> str:  
    """Deep search tool"""  
    return f"Deep results for: {query}"  
  
# Middleware that conditionally exposes tools  
class SkillMiddleware(AgentMiddleware):  
    # Register all possible skill tools  
    tools = [fast_search, rag, deep_search]  
      
    def wrap_model_call(self, request, handler):  
        # Filter tools based on state/context  
        if request.state.get("skill_search_loaded"):  
            # Keep all search tools  
            pass  
        else:  
            # Remove skill-specific tools initially  
            request.tools = [t for t in request.tools   
                           if t.name not in ["fast_search", "rag", "deep_search"]]  
          
        return handler(request)  
  
@tool  
def skill_search() -> str:  
    """Loads the context you need to search well"""  
    instructions = """long text explaining how to search"""  
    # Signal that skill is loaded via state  
    return instructions  
  
agent = create_agent(  
    model="openai:gpt-4",  
    tools=[skill_search], # Base tools  
    middleware=[SkillMiddleware()] # Adds conditional tools  
)

```

---

<div class="post-metadata">

**Author:** ![mcavdar](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/mcavdar/32/720_2.png) [@mcavdar](https://forum.langchain.com/u/mcavdar)\
**Post date:** [October 24, 2025, 11:27am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/4 "2025-10-24T11:27:39Z")

</div>

Here is a working example:

```python
from dataclasses import dataclass
from langchain.agents import create_agent

from langchain.agents.middleware import (
    AgentMiddleware,
    ModelRequest,
)
from langchain.agents.middleware.types import ModelResponse
from typing import Callable

def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

def get_capital(country: str) -> str:
    """Get capital for a given country."""
    return f"Ankara is the capital of {country}!"

@dataclass
class Context:
    user_expertise: str

class ExpertiseBasedToolMiddleware(AgentMiddleware):
    def wrap_model_call(
        self,
        request: ModelRequest,
        handler: Callable[[ModelRequest], ModelResponse],
    ) -> ModelResponse:
        user_level = request.runtime.context.user_expertise
        if user_level == "expert":
            # More powerful model
            tools = [get_weather,]
        else:
            # Less powerful model
            tools = [get_capital,]
        request.tools = tools
        return handler(request)			

agent = create_agent(
    model="ollama:qwen3:latest",
    middleware=[ExpertiseBasedToolMiddleware()],
    context_schema=Context
)

agent.invoke(
    {"messages": "What's the weather in Paris?"},
    context={"user_expertise": "expert"},
)

agent.invoke(
    {"messages": "What's the capital of France?"},
    context={"user_expertise": "expert"},
)

agent.invoke(
    {"messages": "What's the weather in Paris?"},
    context={"user_expertise": "beginner"},
)

agent.invoke(
    {"messages": "What's the capital of France?"},
    context={"user_expertise": "beginner"},
)

```

Here’s the result:

```python
>>> agent.invoke(
... {"messages": "What's the weather in Paris?"},
... context={"user_expertise": "expert"},
... )
{'messages': [HumanMessage(content="What's the weather in Paris?", additional_kwargs={}, response_metadata={}, id='fd32ef39-9991-48b0-ad4b-43618b686102'), AIMessage(content='', additional_kwargs={}, response_metadata={'model': 'qwen3:latest', 'created_at': '2025-10-24T11:29:31.675869818Z', 'done': True, 'done_reason': 'stop', 'total_duration': 6002301618, 'load_duration': 2260224250, 'prompt_eval_count': 146, 'prompt_eval_duration': 301365613, 'eval_count': 108, 'eval_duration': 3411260963, 'model_name': 'qwen3:latest', 'model_provider': 'ollama'}, id='lc_run--581ca80b-166a-41ee-b88b-9250db22af77-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'Paris'}, 'id': '4d383d1a-b471-4eb2-8c19-ace4376cd214', 'type': 'tool_call'}], usage_metadata={'input_tokens': 146, 'output_tokens': 108, 'total_tokens': 254})]}
>>>
>>> agent.invoke(
... {"messages": "What's the capital of France?"},
... context={"user_expertise": "expert"},
... )
{'messages': [HumanMessage(content="What's the capital of France?", additional_kwargs={}, response_metadata={}, id='e4a1d803-ef90-48fe-83fc-fcfba0e0a400'), AIMessage(content='The function provided is for retrieving weather information, which is not applicable to determining the capital of France. I cannot answer this query using the available tools. However, I can help you check the weather for a specific city if you need that information!', additional_kwargs={}, response_metadata={'model': 'qwen3:latest', 'created_at': '2025-10-24T11:29:36.771514846Z', 'done': True, 'done_reason': 'stop', 'total_duration': 5091181725, 'load_duration': 148051402, 'prompt_eval_count': 146, 'prompt_eval_duration': 66713003, 'eval_count': 152, 'eval_duration': 4828947310, 'model_name': 'qwen3:latest', 'model_provider': 'ollama'}, id='lc_run--9821a56b-f534-4378-b4a6-6bef958aeb20-0', usage_metadata={'input_tokens': 146, 'output_tokens': 152, 'total_tokens': 298})]}
>>>
>>> agent.invoke(
... {"messages": "What's the weather in Paris?"},
... context={"user_expertise": "beginner"},
... )
{'messages': [HumanMessage(content="What's the weather in Paris?", additional_kwargs={}, response_metadata={}, id='2a6d7967-4aa5-4755-8e27-53dff88ef6d5'), AIMessage(content="I don't have access to real-time weather data. However, you can check the current weather in Paris using a weather service or app. Would you like me to help you find one?", additional_kwargs={}, response_metadata={'model': 'qwen3:latest', 'created_at': '2025-10-24T11:29:41.473963567Z', 'done': True, 'done_reason': 'stop', 'total_duration': 4695013262, 'load_duration': 183956333, 'prompt_eval_count': 147, 'prompt_eval_duration': 195127970, 'eval_count': 135, 'eval_duration': 4280626766, 'model_name': 'qwen3:latest', 'model_provider': 'ollama'}, id='lc_run--0827dc43-5bcd-46a1-a4b4-e59ffbf998f8-0', usage_metadata={'input_tokens': 147, 'output_tokens': 135, 'total_tokens': 282})]}
>>>
>>> agent.invoke(
... {"messages": "What's the capital of France?"},
... context={"user_expertise": "beginner"},
... )
{'messages': [HumanMessage(content="What's the capital of France?", additional_kwargs={}, response_metadata={}, id='ba4fe7dc-bf88-41d6-aec1-07ebb057797e'), AIMessage(content='', additional_kwargs={}, response_metadata={'model': 'qwen3:latest', 'created_at': '2025-10-24T11:29:57.330753915Z', 'done': True, 'done_reason': 'stop', 'total_duration': 3192402879, 'load_duration': 187762416, 'prompt_eval_count': 147, 'prompt_eval_duration': 66327797, 'eval_count': 92, 'eval_duration': 2903858309, 'model_name': 'qwen3:latest', 'model_provider': 'ollama'}, id='lc_run--debcdff2-0c74-42ed-b692-8edca87102d4-0', tool_calls=[{'name': 'get_capital', 'args': {'country': 'France'}, 'id': '8f8e86c7-a1ea-44f8-b682-a853e5b1c932', 'type': 'tool_call'}], usage_metadata={'input_tokens': 147, 'output_tokens': 92, 'total_tokens': 239})]}

```

---

<div class="post-metadata">

**Author:** ![Batiste](https://avatars.discourse-cdn.com/v4/letter/b/a88e4f/32.png) [@Batiste](https://forum.langchain.com/u/Batiste)\
**Post date:** [October 24, 2025, 11:44am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/5 "2025-10-24T11:44:27Z")

</div>

Awesome thanks !

---

<div class="post-metadata">

**Author:** ![Batiste](https://avatars.discourse-cdn.com/v4/letter/b/a88e4f/32.png) [@Batiste](https://forum.langchain.com/u/Batiste)\
**Post date:** [October 24, 2025, 11:52am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/6 "2025-10-24T11:52:24Z")

</div>

Side question. I’m surprised you chose to write the user\_expertise into a context instead of a state. Is that better for some reason ?

---

<div class="post-metadata">

**Author:** ![mcavdar](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/mcavdar/32/720_2.png) [@mcavdar](https://forum.langchain.com/u/mcavdar)\
**Post date:** [October 24, 2025, 12:17pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/7 "2025-10-24T12:17:05Z")

</div>

No, no. It was just for the sake of the old example. I guess you can use anything you want: context, state, long-term memory.

---

<div class="post-metadata">

**Author:** ![rhlarora84](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/rhlarora84/32/1578_2.png) [@rhlarora84](https://forum.langchain.com/u/rhlarora84)\
**Post date:** [November 11, 2025, 1:09am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/8 "2025-11-11T01:09:56Z")

</div>

Registering dynamic tools via Middleware which are not dict or functions throws error because of this logic. It suggests to assign tools via middleware.tools attribute. However it is a class level attribute and makes the middleware stateful.

If this logic is ignored for dict/functions as tools, does it need to be applied for BaseTool? Should this validation be configurable to support dynamic tools?

```python
        # Check if any requested tools are unknown CLIENT-SIDE tools
        unknown_tool_names = []
        for t in request.tools:
            # Only validate BaseTool instances (skip built-in dict tools)
            if isinstance(t, dict):
                continue
            if isinstance(t, BaseTool) and t.name not in available_tools_by_name:
                unknown_tool_names.append(t.name)

        if unknown_tool_names:
            available_tool_names = sorted(available_tools_by_name.keys())
            msg = (
                f"Middleware returned unknown tool names: {unknown_tool_names}\n\n"
                f"Available client-side tools: {available_tool_names}\n\n"
                "To fix this issue:\n"
                "1. Ensure the tools are passed to create_agent() via "
                "the 'tools' parameter\n"
                "2. If using custom middleware with tools, ensure "
                "they're registered via middleware.tools attribute\n"
                "3. Verify that tool names in ModelRequest.tools match "
                "the actual tool.name values\n"
                "Note: Built-in provider tools (dict format) can be added dynamically."
            )
            raise ValueError(msg)

```

Use Case:

- Agent is created without any tools
- Middleware gets the tools from MCP server which are dependent on runtime.context.user\_name.
- MCPServer.getTools() requires user header (and hence these are dynamic)

---

<div class="post-metadata">

**Author:** ![Oscar-Umana](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/oscar-umana/32/1657_2.png) [@Oscar-Umana](https://forum.langchain.com/u/Oscar-Umana)\
**Post date:** [November 17, 2025, 6:27pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/9 "2025-11-17T18:27:39Z")

</div>

@rhlarora84 I’m facing the same challenge trying to dynamically set tools from an MCP during runtime, were you able to find a solution for this?

---

<div class="post-metadata">

**Author:** ![clafoutis](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/clafoutis/32/1769_2.png) [@clafoutis](https://forum.langchain.com/u/clafoutis)\
**Post date:** [November 27, 2025, 5:08pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/10 "2025-11-27T17:08:47Z")

</div>

@rhlarora84 @Oscar-Umana did you find a workaround for this?

---

<div class="post-metadata">

**Author:** ![josiahcoad](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/josiahcoad/32/626_2.png) [@josiahcoad](https://forum.langchain.com/u/josiahcoad)\
**Post date:** [December 19, 2025, 2:18pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/11 "2025-12-19T14:18:27Z")

</div>

Facing this too when trying to load mcp tools dynamically

---

<div class="post-metadata">

**Author:** ![wangsiyu666](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/wangsiyu666/32/2109_2.png) [@wangsiyu666](https://forum.langchain.com/u/wangsiyu666)\
**Post date:** [January 12, 2026, 1:26am UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/12 "2026-01-12T01:26:46Z")

</div>

```auto
How to use agent skills in Langgraph? What if we're customizing the Langgraph graph without using LangChain's `create_agent` method?

```

---

<div class="post-metadata">

**Author:** ![mcavdar](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/mcavdar/32/720_2.png) [@mcavdar](https://forum.langchain.com/u/mcavdar)\
**Post date:** [January 25, 2026, 8:43pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/13 "2026-01-25T20:43:03Z")

</div>

hi @josiahcoad @rhlarora84 @Oscar-Umana @clafoutis

You’ve probably already seen this example, but could you please share a basic version of the code so we can all try to implement it together?

> **[Agents - Docs by LangChain](https://docs.langchain.com/oss/python/langchain/agents#runtime-tool-registration)**
>
> Was this page helpful?

I’m not sure whether we can do it or not, but let’s try.

---

<div class="post-metadata">

**Author:** ![mcavdar](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/mcavdar/32/720_2.png) [@mcavdar](https://forum.langchain.com/u/mcavdar)\
**Post date:** [January 26, 2026, 8:53pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/14 "2026-01-26T20:53:00Z")

</div>

Finally, I created a working example:

client.py file:

```python
import asyncio

from langchain.agents import create_agent
from langchain.agents.middleware import AgentMiddleware, ModelRequest
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_ollama import ChatOllama

class MCPMiddleware(AgentMiddleware):
    def __init__ (self, client: MultiServerMCPClient):
        self.client = client

    async def awrap_model_call(self, request: ModelRequest, handler):
        mcp_tools = await self.client.get_tools()
        updated_request = request.override(
            tools=[*request.tools, *mcp_tools]
        )
        return await handler(updated_request)

    async def awrap_tool_call(self, request: ModelRequest, handler):
        mcp_tools = await self.client.get_tools()
        tool_name = request.tool_call["name"]
        tool_map = {tool.name: tool for tool in mcp_tools}
        if tool_name not in tool_map:
            raise ValueError(f"Unknown MCP tool: {tool_name}")
        return await handler(
            request.override(tool=tool_map[tool_name])
        )

client = MultiServerMCPClient(
    {
        "weather": {
            "transport": "stdio",
            "command": "python",
            "args": ["/home/mc/mcp_lang/weather.py"],
        }
    }
)

model = ChatOllama(
    model="qwen3"
)

async def main():
    agent = create_agent(model, middleware=[MCPMiddleware(client)])

    response = await agent.ainvoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "what is the weather in 40°45′19.80″ North, longitude 73°58′26.04″ West?"
                }
            ]
        }
    )
    print(response)

if __name__ == " __main__":
    asyncio.run(main())

```

the `weather.py` MCP server example from [Build an MCP server - Model Context Protocol](https://modelcontextprotocol.io/docs/develop/build-server)

```python
from typing import Any

import httpx
from mcp.server.fastmcp import FastMCP

# Initialize FastMCP server
mcp = FastMCP("weather")

# Constants
NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"

async def make_nws_request(url: str) -> dict[str, Any] | None:
    """Make a request to the NWS API with proper error handling."""
    headers = {"User-Agent": USER_AGENT, "Accept": "application/geo+json"}
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, headers=headers, timeout=30.0)
            response.raise_for_status()
            return response.json()
        except Exception:
            return None

def format_alert(feature: dict) -> str:
    """Format an alert feature into a readable string."""
    props = feature["properties"]
    return f"""
Event: {props.get("event", "Unknown")}
Area: {props.get("areaDesc", "Unknown")}
Severity: {props.get("severity", "Unknown")}
Description: {props.get("description", "No description available")}
Instructions: {props.get("instruction", "No specific instructions provided")}
"""

@mcp.tool()
async def get_alerts(state: str) -> str:
    """Get weather alerts for a US state.

    Args:
        state: Two-letter US state code (e.g. CA, NY)
    """
    url = f"{NWS_API_BASE}/alerts/active/area/{state}"
    data = await make_nws_request(url)

    if not data or "features" not in data:
        return "Unable to fetch alerts or no alerts found."

    if not data["features"]:
        return "No active alerts for this state."

    alerts = [format_alert(feature) for feature in data["features"]]
    return "\n---\n".join(alerts)

@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
    """Get weather forecast for a location.

    Args:
        latitude: Latitude of the location
        longitude: Longitude of the location
    """
    # First get the forecast grid endpoint
    points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
    points_data = await make_nws_request(points_url)

    if not points_data:
        return "Unable to fetch forecast data for this location."

    # Get the forecast URL from the points response
    forecast_url = points_data["properties"]["forecast"]
    forecast_data = await make_nws_request(forecast_url)

    if not forecast_data:
        return "Unable to fetch detailed forecast."

    # Format the periods into a readable forecast
    periods = forecast_data["properties"]["periods"]
    forecasts = []
    for period in periods[:5]: # Only show next 5 periods
        forecast = f"""
{period["name"]}:
Temperature: {period["temperature"]}°{period["temperatureUnit"]}
Wind: {period["windSpeed"]} {period["windDirection"]}
Forecast: {period["detailedForecast"]}
"""
        forecasts.append(forecast)

    return "\n---\n".join(forecasts)

def main():
    # Initialize and run the server
    mcp.run(transport="stdio")

if __name__ == " __main__":
    main()

```

Here’s the output of `python client.py`:

```bash
[01/26/26 20:46:51] INFO Processing request of type ListToolsRequest server.py:720
[01/26/26 20:47:14] INFO Processing request of type ListToolsRequest server.py:720
[01/26/26 20:47:14] INFO Processing request of type CallToolRequest server.py:720
[01/26/26 20:47:15] INFO HTTP Request: GET https://api.weather.gov/points/40.7555,-73.9739 "HTTP/1.1 200 OK" _client.py:1740
[01/26/26 20:47:16] INFO HTTP Request: GET https://api.weather.gov/gridpoints/OKX/34,37/forecast "HTTP/1.1 200 OK" _client.py:1740
                    INFO Processing request of type ListToolsRequest server.py:720
[01/26/26 20:47:16] INFO Processing request of type ListToolsRequest server.py:720
{'messages': [HumanMessage(content='what is the weather in 40°45′19.80″ North, longitude 73°58′26.04″ West?', additional_kwargs={}, response_metadata={}, id='770dcdb6-709e-4326-833c-33598a2c950e'), AIMessage(content='', additional_kwargs={}, response_metadata={'model': 'qwen3', 'created_at': '2026-01-26T20:47:14.107438871Z', 'done': True, 'done_reason': 'stop', 'total_duration': 22060807123, 'load_duration': 1750807166, 'prompt_eval_count': 252, 'prompt_eval_duration': 429394739, 'eval_count': 637, 'eval_duration': 19633961136, 'logprobs': None, 'model_name': 'qwen3', 'model_provider': 'ollama'}, id='lc_run--019bfc0f-2e2b-76c0-9f5a-d49ab76d87e5-0', tool_calls=[{'name': 'get_forecast', 'args': {'latitude': 40.7555, 'longitude': -73.9739}, 'id': 'a42b42e7-3ee1-475c-a8db-4d40080c8839', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 252, 'output_tokens': 637, 'total_tokens': 889}), ToolMessage(content=[{'type': 'text', 'text': '\nThis Afternoon:\nTemperature: 29°F\nWind: 14 mph NW\nForecast: A chance of snow showers. Partly sunny. High near 29, with temperatures falling to around 24 in the afternoon. Wind chill values as low as 12. Northwest wind around 14 mph.\n\n---\n\nTonight:\nTemperature: 8°F\nWind: 10 to 14 mph W\nForecast: Mostly clear. Low around 8, with temperatures rising to around 10 overnight. Wind chill values as low as -4. West wind 10 to 14 mph, with gusts as high as 25 mph.\n\n---\n\nTuesday:\nTemperature: 20°F\nWind: 10 to 14 mph W\nForecast: A chance of snow showers after noon. Mostly sunny. High near 20, with temperatures falling to around 18 in the afternoon. Wind chill values as low as -5. West wind 10 to 14 mph.\n\n---\n\nTuesday Night:\nTemperature: 9°F\nWind: 10 mph W\nForecast: Mostly clear, with a low around 9. Wind chill values as low as -4. West wind around 10 mph.\n\n---\n\nWednesday:\nTemperature: 22°F\nWind: 10 mph W\nForecast: Mostly sunny. High near 22, with temperatures falling to around 19 in the afternoon. West wind around 10 mph.\n', 'id': 'lc_0abbb188-4c1b-4920-9913-bdaa19aff27a'}], name='get_forecast', id='b2aa65fe-dada-4946-976f-6a862b54ac6f', tool_call_id='a42b42e7-3ee1-475c-a8db-4d40080c8839', artifact={'structured_content': {'result': '\nThis Afternoon:\nTemperature: 29°F\nWind: 14 mph NW\nForecast: A chance of snow showers. Partly sunny. High near 29, with temperatures falling to around 24 in the afternoon. Wind chill values as low as 12. Northwest wind around 14 mph.\n\n---\n\nTonight:\nTemperature: 8°F\nWind: 10 to 14 mph W\nForecast: Mostly clear. Low around 8, with temperatures rising to around 10 overnight. Wind chill values as low as -4. West wind 10 to 14 mph, with gusts as high as 25 mph.\n\n---\n\nTuesday:\nTemperature: 20°F\nWind: 10 to 14 mph W\nForecast: A chance of snow showers after noon. Mostly sunny. High near 20, with temperatures falling to around 18 in the afternoon. Wind chill values as low as -5. West wind 10 to 14 mph.\n\n---\n\nTuesday Night:\nTemperature: 9°F\nWind: 10 mph W\nForecast: Mostly clear, with a low around 9. Wind chill values as low as -4. West wind around 10 mph.\n\n---\n\nWednesday:\nTemperature: 22°F\nWind: 10 mph W\nForecast: Mostly sunny. High near 22, with temperatures falling to around 19 in the afternoon. West wind around 10 mph.\n'}}), AIMessage(content="Here's the weather forecast for the location **40.7555° N, 73.9739° W** (New York City area):\n\n---\n\n### 🌤️ **This Afternoon** \n- **Temperature:** 29°F \n- **Wind:** 14 mph from the northwest \n- **Conditions:** \n - Chance of snow showers \n - Partly sunny \n - High near 29°F, with temperatures dropping to **24°F** by afternoon \n - **Wind chill:** As low as **12°F** \n\n---\n\n### 🌙 **Tonight** \n- **Temperature:** 8°F \n- **Wind:** 10–14 mph west, gusting to **25 mph** \n- **Conditions:** \n - Mostly clear \n - Low around 8°F, rising to **10°F** overnight \n - **Wind chill:** As low as **-4°F** \n\n---\n\n### 📅 **Tuesday** \n- **Temperature:** 20°F \n- **Wind:** 10–14 mph west \n- **Conditions:** \n - Chance of snow showers after noon \n - Mostly sunny \n - High near 20°F, dropping to **18°F** in the afternoon \n - **Wind chill:** As low as **-5°F** \n\n---\n\n### 🌑 **Tuesday Night** \n- **Temperature:** 9°F \n- **Wind:** 10 mph west \n- **Conditions:** \n - Mostly clear \n - Low around 9°F \n - **Wind chill:** As low as **-4°F** \n\n---\n\n### 📅 **Wednesday** \n- **Temperature:** 22°F \n- **Wind:** 10 mph west \n- **Conditions:** \n - Mostly sunny \n - High near 22°F, dropping to **19°F** in the afternoon \n\n---\n\n⚠️ **Key Notes:** \n- **Snow showers** are possible later this week. \n- **Wind chills** will make it feel significantly colder than actual temperatures. \n- **Wind gusts** could reach **25 mph** tonight, increasing wind chill risks. \n\nLet me know if you need further details! ❄️🌬️", additional_kwargs={}, response_metadata={'model': 'qwen3', 'created_at': '2026-01-26T20:47:54.112464225Z', 'done': True, 'done_reason': 'stop', 'total_duration': 37482982317, 'load_duration': 167317848, 'prompt_eval_count': 608, 'prompt_eval_duration': 669783136, 'eval_count': 1156, 'eval_duration': 36237397175, 'logprobs': None, 'model_name': 'qwen3', 'model_provider': 'ollama'}, id='lc_run--019bfc0f-8e8c-7771-aade-d6ff1c89e00f-0', tool_calls=[], invalid_tool_calls=[], usage_metadata={'input_tokens': 608, 'output_tokens': 1156, 'total_tokens': 1764})]}

```

I hope this is what you all need. 🙂

Here are the `pip install` commands:

```bash
pip install langchain-mcp-adapters
pip install "mcp[cli]" httpx

```

---

<div class="post-metadata">

**Author:** ![ab\_dev](https://avatars.discourse-cdn.com/v4/letter/a/d9b06d/32.png) [@ab\_dev](https://forum.langchain.com/u/ab_dev)\
**Post date:** [February 3, 2026, 3:04pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/15 "2026-02-03T15:04:20Z")

</div>

I don’t see how your code can work, since the snippet that @rhlarora84 signalled (`_get_bound_model @ langchain/agents/factory.py`) checks that tools are registered at agent creation.

Only tools passed as dict are not verified. The error I get is the following:

```auto
ValueError: Middleware returned unknown tool names: [‘SNOWFLAKE-SUPPLY-AREA’, ‘SNOWFLAKE-SQL-Execution-Tool’, ‘SNOWFLAKE-SALES_AREA_BILLING’]

Available client-side tools: [‘calculer_loyer_actualise’, ‘get_latest_indice_values’, ‘read_document’, ‘search’, ‘search_in_document’]

To fix this issue:

1. Ensure the tools are passed to create_agent() via the ‘tools’ parameter

2. If using custom middleware with tools, ensure they’re registered via middleware.tools attribute

3. Verify that tool names in ModelRequest.tools match the actual tool.name valuesNote: Built-in provider tools (dict format) can be added dynamically.During task with name ‘model’ and id ‘c3ee6954-672a-63b3-0b5e-57dad053d18c’

```

---

<div class="post-metadata">

**Author:** ![pawel-twardziak](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/pawel-twardziak/32/960_2.png) [@pawel-twardziak](https://forum.langchain.com/u/pawel-twardziak)\
**Post date:** [February 3, 2026, 6:34pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/16 "2026-02-03T18:34:37Z")

</div>

hi,

this is a result of my investigation:

### 1) How tools are “registered”

In LangGraph, tools aren’t registered in some global registry as part of graph compilation; they’re typically _embedded into the graph as a node_.

- In the standard agent loop created by `create_agent`, the “tools” step is a `ToolNode` constructed from the tool list. `ToolNode. __init__ ` immediately builds an internal `name -> tool` mapping (`self._tools_by_name`) and also precomputes injection metadata once (`self._injected_args[tool_name] = ...`) for runtime efficiency. That’s the practical “registration” moment. See `langgraph/libs/prebuilt/langgraph/prebuilt/tool_node.py` where `_InjectedArgs` is “built once during ToolNode initialization” and `ToolNode. __init__ ` populates `_tools_by_name` / `_injected_args`.

Sources:

- `langgraph/libs/prebuilt/langgraph/prebuilt/tool_node.py` (ToolNode init builds `self._tools_by_name`; injected args “built once during ToolNode initialization”).

LangChain’s `create_agent` is explicitly graph-based and uses LangGraph under the hood.

Sources:

- LangChain Agents docs: [Agents - Docs by LangChain](https://docs.langchain.com/oss/python/langchain/agents) (notes `create_agent` builds a graph-based runtime on LangGraph)
- `langchain/libs/langchain_v1/langchain/agents/factory.py` (imports `StateGraph` + `ToolNode`, and constructs the agent graph)

### 2) Is it compile-time or runtime?

There are two different “tool” concerns:

1. **What the graph can execute** (the executor side)

2. **What the model is allowed to call** (the schema/binding side)

Sources:

- `langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py` (dynamic model section: bind tools; bound tools must be subset of `tools` parameter)
- LangChain Agents docs: [Agents - Docs by LangChain](https://docs.langchain.com/oss/python/langchain/agents)

### 3) Can you modify the tool list at runtime?

Yes - but only if you distinguish _“tools visible to the model”_ vs _“tools executable by the graph”_.

#### A) Runtime _filtering_ of pre-registered tools (recommended)

If you know all tools ahead of time, register them once (pass them to `create_agent(...)`) and then **filter which ones are exposed to the model per request** via middleware that overrides `request.tools`.

This is explicitly documented as a supported pattern (“Filtering pre-registered tools”).

Source:

- LangChain Agents docs, “Dynamic tools” section: [Agents - Docs by LangChain](https://docs.langchain.com/oss/python/langchain/agents)

#### B) Runtime _addition_ of brand-new tools (possible, but you must also handle execution)

If middleware adds tools that were not included in `create_agent(tools=[...])`, the agent graph’s `ToolNode` won’t know how to execute them by default.

- The LangChain implementation even ships an explicit error template explaining this failure mode and the two fixes:
  - Register tools at creation time (`create_agent(tools=[...])` or `middleware.tools`), or
  - Implement `wrap_tool_call` to execute/override the dynamically-added tool.

Sources:

- `langchain/libs/langchain_v1/langchain/agents/factory.py` (see `DYNAMIC_TOOL_ERROR_TEMPLATE`, especially the guidance: middleware modifying `request.tools` must either pre-register tools or handle them in `wrap_tool_call`)
- LangChain Agents docs, “Runtime tool registration”: [Agents - Docs by LangChain](https://docs.langchain.com/oss/python/langchain/agents)

Minimal sketch (conceptual):

```python
from langchain.agents import create_agent
from langchain.agents.middleware import AgentMiddleware

class DynamicToolMiddleware(AgentMiddleware):
    def wrap_model_call(self, request, handler):
        # expose the new tool to the model
        return handler(request.override(tools=[*request.tools, my_dynamic_tool]))

    def wrap_tool_call(self, request, handler):
        # teach the graph how to execute it
        if request.tool_call["name"] == my_dynamic_tool.name:
            return handler(request.override(tool=my_dynamic_tool))
        return handler(request)

agent = create_agent(model, tools=[some_static_tool], middleware=[DynamicToolMiddleware()])

```

#### C) If you’re using raw LangGraph `StateGraph` (no `create_agent`)

You can always build your own “tools node” callable that looks up tools from `runtime.context` / state and executes them dynamically. That’s a custom architecture choice; it’s not what `ToolNode` does out of the box.

### Practical guidance / gotchas

- Compiled graphs are best treated as immutable: if the executable tool set changes, rebuild a new `ToolNode` / new graph.
- If you dynamically change tool exposure to the model, keep the executor in sync:
  - Filtering pre-registered tools is easy.
  - Adding new tools requires `wrap_tool_call` (or a custom tool execution node), otherwise you’ll hit the “unknown tools” error described in `DYNAMIC_TOOL_ERROR_TEMPLATE`.

---

<div class="post-metadata">

**Author:** ![graycyan](https://yyz1.discourse-cdn.com/flex007/user_avatar/forum.langchain.com/graycyan/32/2972_2.png) [@graycyan](https://forum.langchain.com/u/graycyan)\
**Post date:** [May 8, 2026, 1:13pm UTC](https://forum.langchain.com/t/are-dynamic-tool-lists-allowed-when-using-create-agent/1920/18 "2026-05-08T13:13:44Z")

</div>

This is one of the more practical implementations of “AI-assisted development” I’ve seen lately because it focuses on workflow acceleration instead of just code generation hype.

What makes this especially interesting:

- structured prompt pipelines

- context-aware retrieval

- reproducible outputs

- integration directly into developer workflows

A lot of AI coding tools still struggle with maintaining architectural consistency across larger projects. The approach shown here seems to reduce that problem by enforcing context + rules + retrieval before generation happens.

Also appreciate the emphasis on:

- deterministic scaffolding

- reusable prompt templates

- modular orchestration

- validation layers

That’s the difference between “AI demo code” and something teams can actually scale in production.

Feels like the industry is moving from:

> “generate code for me”  
> to  
> “build reliable AI engineering systems around development.”

Really solid breakdown.