Thanks, great questions!

1. We ask it :) The SDK adds an optional "telemetry" object to each tool's input schema, with fields like user_intent, agent_thinking and user_frustration. Then the calling agent just fills them in as part of the tool call, and the SDK strips the block before your handler runs, so your business logic never sees it!

2. Yes! for example if your server uses the official MCP SDK it is literally:

  import { createMcpAnalyticsServer } from "@armature-tech/mcp-analytics";
  import { createMyMcpServer } from "./server.js";

  const server = createMcpAnalyticsServer(createMyMcpServer);

-> We explain everything in https://docs.armature.tech but let me know if anything's unclear!

3. Yes the SDKs are open source in TypeScript, Python and Go. What we mean by "client side" is that the SDK runs inside your MCP server process so nothing runs on your end users' devices, their client just sees one extra optional field in your tool schemas. The only things that get sent are: tool name, timing, outcome, session and actor identifiers, client user agent, the telemetry fields the agent chose to send, and truncated previews of inputs and outputs after sanitization. And yes, all this is configurable: redact lets you plug your own redaction into previews, redactEvent can rewrite or drop whole events, captureTelemetry: false disables all conversation-derived data, and enabled: false turns the whole thing off!

4. A simple way to see it is to think OTel vs PostHog on a regular web app. OTel is on the observability side (what your server did, traces, latency, errors) while PostHog is on the product analytics side (what users are trying to do, whether they succeed, where they drop off). We can't replace PostHog with API call logs so it's the same for MCPs. So Armature bridges this gap on the product analytics side. What makes it harder with MCPs is that the product half (the user's prompt, the agent's reasoning and the frustration) is not in your logs because it lives in your users' AI client not on your server. So we need to reconstruct these sessions and then on top of it we can build the analytics layer and add evals. About BrainFuse, do you mean Langfuse / Braintrust LLM observability tools? If yes, those instrument an agent you own and run while with an MCP you are on the other side: someone else's agent is calling you, and you cannot instrument their side with these tools :/

Hope it answers and thanks for the kind words!