MCP server for spec-driven development management
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"foundry-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# foundry-mcp [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://modelcontextprotocol.io/) [](https://pypi.org/project/foundry-mcp/) **Turn AI coding assistants into reliable software engineers with structured specs, progress tracking, and automated review.** ## Table of Contents - [Why foundry-mcp?](#why-foundry-mcp) - [Key Features](#key-features) - [Installation](#installation) - [Quick Start](#quick-start) - [How It Works](#how-it-works) - [Configuration](#configuration) - [Advanced Usage](#advanced-usage) - [Documentation](#documentation) - [Scope and Limitations](#scope-and-limitations) - [Testing](#testing) - [Contributing](#contributing) - [License](#license) ## Why foundry-mcp? **The problem:** AI coding assistants are powerful but unreliable on complex tasks. They lose context mid-feature, skip steps without warning, and deliver inconsistent results across sessions. **The solution:** foundry-mcp provides the scaffolding to break work into specs, track progress, and verify outputs—so your AI assistant delivers like a professional engineer. - **No more lost context** — Specs persist state across sessions so the AI picks up where it left off. - **No more skipped steps** — Task dependencies and blockers ensure nothing gets missed. - **No more guessing progress** — See exactly what's done, what's blocked, and what's next. - **No more manual review** — AI review validates implementation against spec requirements. ## Key Features - **Specs keep AI on track** — Break complex work into phases and tasks the AI can complete without losing context. - **Progress you can see** — Track what's done, what's blocked, and what's next across multi-ses…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.