Kian Jones ddcfeb26b1 fix(core): catch all MCP tool execution errors instead of re-raising (#9419)
* fix(core): catch all MCP tool execution errors instead of re-raising

MCP tools are external user-configured servers - any failure during
tool execution is expected and should be returned as (error_msg, False)
to the agent, not raised as an exception that hits Datadog as a 500.

Previously:
- base_client.py only caught McpError/ToolError, re-raised everything else
- fastmcp_client.py (both SSE and StreamableHTTP) always re-raised

Now all three execute_tool() methods catch all exceptions and return
the error message to the agent conversation. The agent handles tool
failures via the error message naturally.

This silences ~15 Datadog issue types including:
- fastmcp.exceptions.ToolError (validation, permissions)
- mcp.shared.exceptions.McpError (connection closed, credentials)
- httpx.HTTPStatusError (503 from Zapier, etc.)
- httpx.ConnectError, ReadTimeout, RemoteProtocolError
- requests.exceptions.ConnectionError
- builtins.ConnectionError

🐾 Generated with [Letta Code](https://letta.com)

Co-Authored-By: Letta <noreply@letta.com>

* fix(core): log unexpected MCP errors at warning level with traceback

Expected MCP errors (ToolError, McpError, httpx.*, ConnectionError, etc.)
log at info level. Anything else (e.g. TypeError, AttributeError from
our own code) logs at warning with exc_info=True so it still surfaces
in Datadog without crashing the request.

🐾 Generated with [Letta Code](https://letta.com)

Co-Authored-By: Letta <noreply@letta.com>

---------

Co-authored-by: Letta <noreply@letta.com>
2026-02-24 10:52:07 -08:00
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TypeScript:

import Letta from "@letta-ai/letta-client";

const client = new Letta({ apiKey: process.env.LETTA_API_KEY });

// Create your agent
const agentState = await client.agents.create({
  model: "openai/gpt-5.2",
  memory_blocks: [
    {
      label: "human",
      value:
        "Name: Timber. Status: dog. Occupation: building Letta, infrastructure to democratize self-improving superintelligence",
    },
    {
      label: "persona",
      value:
        "I am a self-improving superintelligence. Timber is my best friend and collaborator.",
    },
  ],
  tools: ["web_search", "fetch_webpage"],
});

console.log("Agent created with ID:", agentState.id);

// Send your agent a message
const response = await client.agents.messages.create(agentState.id, {
  input: "What do you know about me?",
});

for (const message of response.messages) {
  console.log(message);
}

Python:

from letta_client import Letta
import os

client = Letta(api_key=os.getenv("LETTA_API_KEY"))

# Create your agent
agent_state = client.agents.create(
    model="openai/gpt-5.2",
    memory_blocks=[
        {
          "label": "human",
          "value": "Name: Timber. Status: dog. Occupation: building Letta, infrastructure to democratize self-improving superintelligence"
        },
        {
          "label": "persona",
          "value": "I am a self-improving superintelligence. Timber is my best friend and collaborator."
        }
    ],
    tools=["web_search", "fetch_webpage"]
)

print(f"Agent created with ID: {agent_state.id}")

# Send your agent a message
response = client.agents.messages.create(
    agent_id=agent_state.id,
    input="What do you know about me?"
)

for message in response.messages:
    print(message)

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