Armature

What it does

Armature provides analytics and evaluation tools for AI agents that use Model Context Protocol (MCP) integrations. It reconstructs complete session histories showing what users asked their agents to do and how the agent responded. Founders integrate Armature by wrapping their MCP in three lines of code, then access a dashboard displaying all sessions, ranked use cases based on session clustering, and frequent issues users encounter.

The platform includes MCP Analytics to replay sessions step-by-step, Eval Suites that test workflows against MCPs on every deploy, and a search evaluation tool called Armature Search that mimics how Claude Code and Codex search the web.

Who it is for

Armature targets product teams building tools, integrations, or MCPs that AI agents like Claude Code, Codex, and Cursor might use. It serves both discovery (helping tools get picked by agents) and usability (ensuring tools work reliably once deployed). The platform appeals to founders of developer tools, CLI applications, and integrations that need to be discoverable and functional within AI agent workflows.

Pricing

The site indicates a free tier with "Start for free · No credit card required" but does not display detailed pricing tiers or paid plan costs.

How it stands out

Armature combines two distinct offerings: a discovery service and a self-serve analytics product. On discovery, the company runs an agent discoverability service with a panel of 16 repositories representing real-world codebases across languages and stacks. It simulates how coding agents choose tools and works with founders to improve their pick rate. On analytics, it reconstructs individual agent sessions, groups them by use case, and runs eval suites to catch regressions before users encounter them. The Armature Search tool claims 90+ percent similarity to actual agent search behavior, enabling pre-deployment testing.

What a founder should check

First, verify the stability and representativeness of the repository panel. A panel of 16 repositories may not capture edge cases or emerging agent capabilities, so founders should understand whether the measured pick rates translate to real traffic. Second, evaluate switching costs and integration effort. The "three lines of code" claim needs validation—how quickly can a team actually integrate and start seeing useful data, and how locked in would they become? Third, assess the moat around the Armature Search similarity claim. If agents update their search behavior or the similarity drops below claimed levels, the eval accuracy diminishes, making the product's core value proposition less defensible.