Graqle MCP Server

Most developer tooling work still happens through a UI a human drives. Graqle MCP server moves it into the conversation instead. Dev intelligence layer: 7 MCP tools for graph-powered codebase reasoning.

The short version

def score_application(app): decision = model.predict(app) # your deployed AI, untouched gov.attest( # <-- the one added line domain="loan", model_id="credit-risk-v4", inputs={"applicant_ref": gov.pseudonymize_ref(app.id)}, # PII-safe output={"decision": decision.label, "reason_code": decision.reason}, ) return decision ```

A governance-led multi-agent reasoning system for code, with a built-in cryptographic audit substrate for the AI you ship to production. Scan any codebase into a persistent knowledge graph. Every module becomes a reasoning agent. Agents decompose, debate, and synthesize answers with clearance-level governance. Every change — and every production decision — is impact-analysed, gate-checked, and cryptographically committed.

Getting it running

Because this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.

How it compares

This sits in the developer tooling group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. It is maintained by com.graqle; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.

Things to watch

• Your data travels to the provider's service, so the usual questions apply about what you send and what they retain.
• Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.