SpecForge MCP server

Try it in 5 minutes · How it works · See it catch something · Docs

See it in 60 seconds, no agent needed. Install, create a project, drop one letter, check:

curl -fsSL https://raw.githubusercontent.com/leaderiop/SpecForge/main/install.sh | sh
mkdir sf-demo && cd sf-demo
specforge init --extensions @specforge/software          # spec/hello.spec, which checks clean
sed -i.bak 's/\[user_logged_in\]/[user_loged_in]/' spec/hello.spec   # drop one letter
specforge check

[E003] Error: unresolved reference 'user_loged_in' in entity 'authenticate_user'
 20 │   produces [user_loged_in]
    │ Help: did you mean 'user_logged_in'?

[W007] Warning: event 'user_logged_in' is not produced by any behavior

1 error, 1 warning, 0 infos

A reviewer reads. A check resolves.

The problem

You ask an AI agent to add refunds to your payment service. It writes clean code, the tests pass, the pull request looks fine. Two weeks later, finance finds a refund larger than the payment it refunded.

The rule was written down, in the spec: a Markdown file in docs/, next to the PRD. The agent read it three sessions ago, or never found it. Nothing failed, so nobody noticed.

This isn't a problem with the model. It's a problem with where your rules live. Today, the knowledge that matters sits in places that nothing enforces:

• Specs in Markdown (PRDs, docs/*.md, spec-kit files) are prose. An agent can misread a rule or skip it, silently, and nothing checks that the spec is complete or that its parts agree.
• Docs and wikis drift away from the code, and nothing breaks when they do.
• Traceability (which requirement has a test, which rule is enforced where) is a spreadsheet someone updates by hand, until they stop.
• Every session starts from zero. The agent re-reads dozens of files to rediscover what you meant, and still guesses.

The more work you give to AI, the worse this gets. It writes faster than anyone can review.

The idea: hard skills, not soft skills

There are two ways to teach an AI how your team works.

A soft skill is advice placed in the AI's context: a Markdown spec, a PRD, a skill file. It helps, but it's prose. The AI may follow it, or not.

A hard skill is a rule that a program enforces. The AI can't skip it, because the check fails.

Shortened. The full README is on GitHub.