"Co-op" matching MCP servers:

Gives OpenAI Codex behavioral history from workspace.json, exposing file fragility and co-change partners as tools to enforce safer edits and block incomplete patches.

Semantic git queries via MCP. Beyond git log — answer who/what/why about any line, file, or branch with blame, co-change, PR linkage.

Enables an MCP-capable assistant to review GitLab merge requests, GitHub pull requests, and pre-push local branches against ticket requirements, domain rubrics, compiler/lint pre-passes, blast-radius analysis, and SonarQube findings, and to draft inline comments or follow-up tasks in the user's chosen language. Nothing reaches a remote host until a human approves the exact content in an interactive terminal.

AI co-founder MCP server for solo founders. Multi-perspective code review (CTO, Security, Product, DevOps, Customer), stage-aware guidance, tech decision validation, and portfolio management across projects.

Analyzes code changes using static code-graph and git co-change history to identify likely affected files, returning tiered confidence reports with evidence. Exposes tools for AI assistants to inspect current changes, static impact, historical co-change patterns, and detailed explanations.

Provides crowdsourced package intelligence and security alerts for AI coding assistants by analyzing project dependencies and framework co-occurrence. It enables automated project scans, package alternative discovery, and data-driven recommendations across multiple programming ecosystems.

Lets coding agents query a hosted knowledge graph of a git repository over MCP or the shell to retrieve recorded commit intent, co-change coupling, ranked blast radius, and computed architecture. Answers are drawn from the graph rather than file reads, reporting measured per-answer token savings.

Enables coding agents to query a local, multi-repo code graph for symbol exploration, blast radius analysis, co-change mining, and durable code-anchored memory via MCP tools.

Enables querying Go module versions, metadata, and go.mod contents by wrapping proxy.golang.org, providing tools for version listing and dependency analysis.

Enables source-first orchestration and knowledge indexing with inspectable architectural capabilities through a unified MCP interface, including named-attribute retrieval and atomic knowledge ingestion.

Provides AI coding agents with pre-edit situational awareness by combining structural call graphs and co-change history to prevent incomplete edits. It surfaces files that historically change together, reducing missed coupled modules.

Live codebase intelligence for AI agents. Import graph PageRank for file importance, git forensics for co-change coupling and fragile code, convention detection across 16 domains, and blast radius analysis.

An MCP server that indexes WordPress hooks, filters, block registrations, and JS API calls to provide AI assistants with a verified source-code database. It enables models to search, validate, and retrieve code context for WordPress and its plugins to eliminate hook-name hallucinations.

MCP server that checks AI agent edits for missing companion files by learning co-change patterns from git history, preventing incomplete commits.

Source-code intelligence for agents, powered by OpenGrok and Go.

Local-first CLI that mines git history for file-level co-change patterns and builds a queryable knowledge graph for AI coding agents, exposed via an MCP server.

Enables AI coding agents to query a local structural code graph and receive focused, token-bounded context about source entities, relationships, Git co-change, and selected project memory without reading full files.

One MCP server for golangci-lint, govulncheck, and nilaway — a unified Diagnostic\[] array with consistent file:line:column navigation, parallel execution, and zero-config auto-install - for golang development.

Gives your AI coding agent a bounded map of an unfamiliar TypeScript SaaS repo: where the code lives, what's risky to touch, and where the money / auth / user flows are. Provides eight MCP tools for orientation, task focus, risk assessment, and SaaS-specific observations without AST or type checking.

Persistent codebase knowledge layer for AI agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) and serves via MCP. 28 languages, 14 tools, ~85% token reduction.