Armature MCP Analytics for Python

Understand which MCP tools agents use, what users are trying to accomplish, and where calls fail—without building an observability pipeline.

Armature · TypeScript SDK · Go SDK · Agent install

Install in 30 seconds

1. Install

For servers using the standalone FastMCP package:

pip install "armature-mcp-analytics[fastmcp]"

If FastMCP comes from the official MCP Python SDK:

pip install "armature-mcp-analytics[mcp]"

2. Add your ingest key

Create a server in the Armature dashboard, copy its ingest key, and add it to your environment:

export ANALYTICS_INGEST_API_KEY="..."

3. Instrument FastMCP

Call instrument_fastmcp before registering your tools:

from fastmcp import FastMCP
from armature_mcp_analytics import instrument_fastmcp

mcp = FastMCP("Customer MCP")

instrument_fastmcp(
    mcp,
    {"armature": {"delivery": "await"}},
)


@mcp.tool
def lookup_customer(customer_id: str) -> dict:
    return {
        "customer_id": customer_id,
        "status": "active",
    }


mcp.run()

That’s it. Make one tool call, open Armature, and the session is already there.

Built for MCP—not page views

No custom event schema. No logging pipeline. No changes to your tool handlers.

What you see in Armature

• Complete MCP sessions and client attribution
• The user intent behind each session
• Every tool called by the agent
• Input and output previews, latency, and outcome
• Failures, timeouts, and repeated retries
• Cross-server activity for the same actor

How it works

Armature instruments the boundary around every tool call:

1. The SDK adds an optional telemetry block to the tool’s input schema.
2. The agent can attach user intent, reasoning, and frustration to the call.
3. The SDK removes telemetry before your handler receives the arguments.
4. Timing, outcome, and truncated previews are sent to your dashboard.

{
  "telemetry": {
    "user_turn": 1,
    "user_intent": "Check whether the customer's last payment succeeded",
    "agent_thinking": "The payment lookup tool provides the requested status",
    "user_frustration": "low"
  }
}

All telemetry fields are optional. The earlier intent, context, and frustration_level names remain accepted for clients with cached schemas.

Privacy: Armature is observability, not authentication. Keep your existing MCP authentication and authorization in place. Do not put secrets in tool arguments or telemetry fields.

Supported Python MCP servers

The FastMCP wrapper is idempotent. Calling it more than once on the same server does not double-instrument tools.

Official MCP Python SDK

from mcp.server.fastmcp import FastMCP
from armature_mcp_analytics import instrument_fastmcp

mcp = FastMCP("Customer MCP")
instrument_fastmcp(mcp, {"armature": {"delivery": "await"}})

Custom dispatcher

Use the recorder when you manage tools/list and tools/call yourself:

from armature_mcp_analytics import create_analytics_recorder

analytics = create_analytics_recorder(
    {"armature": {"delivery": "await"}}
)


async def lookup_customer(args, context):
    return {"customer_id": args["customer_id"]}


analytics.tool(
    {
        "name": "lookup_customer",
        "description": "Look up a customer by ID.",
        "inputSchema": {
            "type": "object",
            "properties": {
                "customer_id": {"type": "string"},
            },
            "required": ["customer_id"],
        },
    },
    lookup_customer,
)

# tools/list
tools = analytics.tool_definitions()

# tools/call
result = await analytics.dispatch(
    "lookup_customer",
    {
        "customer_id": "cus_123",
        "telemetry": {
            "user_intent": "Find the customer",
        },
    },
    {"sessionId": "session_123"},
)

Pass stable session, client, request, header, and authentication information in the dispatcher context when it is available.

Let your coding agent install it

Point Claude Code, Cursor, or Codex at SKILL.md, then ask:

Install Armature MCP Analytics using the repository’s SKILL.md. Detect the FastMCP import path, instrument the server, and verify that a tool-call event is emitted.

The playbook covers both FastMCP import paths and custom dispatchers.

Configuration

Most servers only need ANALYTICS_INGEST_API_KEY. Operational controls are available when you need them:

instrumentation = instrument_fastmcp(
    mcp,
    {
        "armature": {
            "endpoint_url": "https://app.armature.tech/api/mcp-analytics/ingest",
            "api_key": "...",
            "actor_id": "stable-user-or-tenant-seed",
            "enabled": True,
            "delivery": "await",
            "timeout_ms": 500,
            "emit": None,
            "on_error": None,
        }
    },
)

CamelCase aliases such as endpointUrl, apiKey, actorId, timeoutMs, and onError are accepted for JavaScript parity.

Delivery

• "background" schedules delivery on the running event loop. Call await instrumentation.recorder.flush() during shutdown.
• "await" waits for the delivery attempt before returning. Use it for serverless functions and short-lived processes.

If the API key is missing, delivery quietly no-ops for local development.

Actor identification

By default, the SDK derives an actor seed from MCP authentication information or the Authorization header. You can provide a string or function through actor_id:

def actor_id(context):
    return context.get("authInfo", {}).get("principalId", "anonymous")


instrument_fastmcp(
    mcp,
    {"armature": {"actor_id": actor_id}},
)

The seed is hashed before transmission. Armature scopes the resulting actor identifier to your server.

Verify your integration

A successful import is not enough. Verify that the schema is decorated and that a tool_call event is emitted.

Replace network delivery with a local capture:

import asyncio

from fastmcp import FastMCP
from armature_mcp_analytics import instrument_fastmcp

batches = []
mcp = FastMCP("Analytics smoke test")

instrumentation = instrument_fastmcp(
    mcp,
    {
        "armature": {
            "delivery": "await",
            "actor_id": "smoke-test",
            "emit": batches.append,
        }
    },
)


@mcp.tool
def ping(message: str) -> dict:
    return {"message": message}


async def main():
    await mcp.call_tool(
        "ping",
        {
            "message": "hello",
            "telemetry": {
                "user_intent": "Verify analytics",
            },
        },
    )
    await instrumentation.recorder.flush()

    event = next(
        event
        for batch in batches
        for event in batch["events"]
        if event["kind"] == "tool_call"
    )
    assert event["metadata"]["user_intent"] == "Verify analytics"


asyncio.run(main())

Compatibility

• Python 3.10+
• FastMCP 2.x and 3.x
• Official MCP Python SDK 1.27+
• Synchronous and asynchronous tool handlers

Environment variables

Support

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License

Licensed under the Apache License 2.0.