"Information about Context7" matching MCP servers:

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

truth is a deterministic fact-checker for the claims an AI coding agent makes about its own work.

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

An MCP server that indexes a repository locally and provides keyword, semantic, hybrid, and SQL search tools, enabling coding agents to answer questions about the codebase efficiently without reading files one by one.

MCP server that provides npm package information for AI agents during TypeScript development.

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

Enables AI agents to search and retrieve details about Odoo code elements (models, fields, views, etc.) with exact file locations using MCP tools.

Provides coding agents with a compact map of a TypeScript/TSX codebase, enabling direct answers about symbol locations, imports, dependencies, and references while reducing token usage.

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation

Local-first, code-aware engineering cognition for AI coding agents - persistent memory, contextual retrieval, and continuous cognition about your codebase.

Provides code context and analysis for AI assistants by extracting directory structures and code symbols using WebAssembly Tree-sitter parsers with zero native dependencies.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Lets AI coding agents ask a Rails app about itself: 35 read-only tools answer from the schema, models, routes, controllers, views, jobs and conventions, over MCP or the command line, even when the app cannot boot.

Exposes Lachesis's code navigation tools over MCP, letting LLM agents ask precise compiler-level questions about dataflow, taint, callers, and guards in source code.

An MCP server for gitingest. It allows MCP clients like Claude Desktop, Cursor, Cline etc to quickly extract information about Github repositories including repository summaries, project directory structure, file contents, etc

Provides tools for mapping and analyzing GitHub repositories. It allows users to set a GitHub Personal Access Token and retrieve detailed information about a specified repository, including its structure and summary statistics.

A local, read-only MCP server that provides AI coding agents with structural evidence about a repository, including dependency analysis and impact assessment, while naming the boundary of every answer without any LLM calls.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

Give your coding agent the dependency graph it is about to change: scan, diff, explain, audit.

Enables any MCP client to detect Claude Code configuration that is silently ignored, such as agent files that fail to load, unknown frontmatter fields, parent-folder settings, conflicting MCP server entries and drift since a baseline. Exposes read-only tools that return findings with exact edits, the fix prompt, and explanations of each check, without modifying any files.

Read-only MCP server that provides AI agents with context about Unity project relationships, including scenes, prefabs, scripts, GUIDs, and code impact analysis.

An MCP server that ingests Spring Boot REST microservices from GitHub, builds a request-flow ontology, and enables natural-language queries about code with flow-aware answers and citations.

Static codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.

Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.

Remote MCP server that gives coding agents verified, version-pinned answers about fast-moving frameworks, starting with Expo SDK 54.

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

Enables agents to query TypeScript and JavaScript code intelligence—type diagnostics after edits, go-to-definition, references, hover information, and document symbols—through MCP tools.

Enables AI coding agents to retrieve compact structural codebase context through repository architecture summaries, AST symbol extraction, cyclomatic complexity scoring, and TODO/FIXME hotspot search.

Helps AI read GitHub repository structure and important files. Want to quickly understand what a repo is about? Prompt it with "read https://github.com/adhikasp/mcp-git-ingest and determine how the code technically works".