Quality gates for AI agents. Lint, test, build checks with memory persistence.
io.github.rigour-labs/rigour MCP Server
This Model Context Protocol (MCP) server provides “quality gates for AI agents,” combining lint, test, and build checks with memory persistence. It is positioned for agentic workflow support and software-engineering guardrails, using the Model Context Protocol for developer tooling integration.
🛠️ Key Features
Quality gates for AI agents
Lint, test, and build checks
Memory persistence
🚀 Use Cases
Agentic workflow quality control
Code-quality enforcement for AI agent runs
Developer guardrails during software engineering
⚡ Developer Benefits
Static-analysis-style checks via linting
Type-system oriented workflows (as indicated by “typesystem”)
Tooling alignment with MCP (“model-context-protocol”, “mcp”)
⚠️ Limitations
Only the available description and excerpt confirm lint/test/build checks and memory persistence; no other MCP capabilities are stated
Your coding agents write code. Rigour makes the codebase learn.
Rigour is a local-first engineering intelligence layer for coding agents. It gives agents the smallest useful context, enforces deterministic boundaries around observable work, and turns verified outcomes into reusable knowledge for the next task.
It is not another chat wrapper or a prettier linter dashboard. Rigour connects code, agent actions, decisions, proof, and learning into one evidence-backed system.
Open Studio to trace an agent run from the recommendation it received to the code it changed, the checks that ran, the risk it prevented, and the knowledge it left behind.
bash
npx @rigour-labs/cli init
npx @rigour-labs/cli hooks init
npx @rigour-labs/cli studio
hooks init supports Cursor, Claude Code, Cline, and Windsurf. It checks observable writes as agents work; check is the full project verification step.
3. Give every agent the same good workflow
bash
rigour skills install --target codex,cursor
Rigour Skills turn its evidence loop into small, reusable agent workflows:
rigour-context asks for the smallest explainable scope before an agent reads code.
rigour-verify closes a change with proof and a Fix Packet repair loop.
rigour-handoff transfers verified state without replaying an entire session.
Codex receives native repository skills in .agents/skills. Cursor receives focused slash commands in .cursor/commands. Portable copies land in docs/rigour-skills for other MCP-capable agents. Existing files are preserved unless --force is explicitly used.
Agents can ask Rigour for scoped context, register their work, receive Fix Packets, record checkpoints and handoffs, and leave evidence for Studio.
5. Mediate high-impact MCP tools (6.2)
Rigour can sit in front of selected MCP servers, expose only approved tools, normalize every call into a common action record, and issue a signed execution receipt. Start in observe mode to see what policy would block without interrupting work; switch to enforce only after reviewing the evidence.
Trusted state and canonical receipts live outside the repository. Studio reads them server-side and receives only safe evidence summaries for its Review panel and execution map—never keys, environment secrets, downstream commands, or receipt signatures. Follow MCP Integration for the configuration and exact security boundary.
What Rigour does differently
Context that can explain itself
Rigour builds a structural index immediately and enriches semantic retrieval in the background. When an agent asks for help, it returns the smallest evidence-backed scope it can justify—not a repository dump. Each recommendation records its sources, exclusions, cache reuse, and estimated context savings.
Learning with a proof boundary
Rigour records evidence from its hooks, MCP calls, context retrieval, Fix Packets, accepted changes, checkpoints, handoffs, tests, and human feedback. A lesson starts as a candidate. It becomes reusable only after deterministic verification, repeated successful outcomes, or explicit confirmation.
Model text, vector similarity, rejected fixes, and failed tests can inform investigation. They do not become enforcement rules on their own.
Governance agents can work with
Rigour’s deterministic checks catch security issues, structural regressions, hallucinated imports, phantom APIs, context drift, and more. On supported mediated paths, the Agent Transaction Firewall applies per-agent scopes, typed command allowlists, fail-closed arbitration, and signed attestations.
When work fails a check, Rigour gives the agent a Fix Packet: the rule, affected files, evidence, and concrete next action.
Local first. Team-ready when you are.
Rigour works fully in local-only mode with SQLite. Nothing requires an account.
For teams, PostgreSQL becomes the durable source for private-user and approved shared knowledge; encrypted SQLite remains the local cache and offline outbox. If pgvector is enabled, Rigour can use semantic recall as advisory input while repository scope and lesson state continue to control what applies.
bash
rigour team init-schema --database-url 'postgresql://…' --pgvector
rigour team configure --database-url 'postgresql://…' \
--organization acme --team platform --actor ashutosh --pgvector
rigour team import-local /path/to/repository --dry-run
rigour team import-local /path/to/repository
rigour team sync
rigour team doctor
team import-local safely adopts personal lessons created before team mode was
configured. It is repository-scoped, dry-runnable, idempotent, and never promotes
candidate knowledge or publishes it to the team.
When team storage is unavailable, local enforcement and evidence capture continue. Studio reports the state as offline — changes queued.
Guarantees and boundaries
Rigour is deliberately precise about what it does and does not claim.
Core checks and storage are local-first; cloud deep analysis is opt-in.
Rigour can enforce work that passes through its installed hooks or MCP gateway. A directly configured parallel MCP server bypasses that gateway unless the host or administrator removes that route.
Advice is evidence of what Rigour recommended, not proof that an agent followed it or that it caused an outcome.
Observed spend, measured estimates, and modelled savings are shown separately so cost numbers do not over-promise.