"A server for finding information about 'Context 7'" matching MCP servers:

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 AI coding agents to automatically compile the most relevant slice of a repository into context for a given task.

MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

MCP server that inspects local codebases and generates source-grounded context packs for coding agents, with tools for repository search, symbols, dependencies, Git analysis, and context generation.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

Provides codebase indexing and retrieval tools that give AI agents token-efficient, query-relevant context packages (symbols, imports, and dependencies) instead of scanning entire repositories.

Provides persistent memory and a codebase knowledge graph for AI coding assistants, enabling shared context across multiple tools like Claude, Cursor, and ChatGPT, with significant token reduction.

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.

An MCP server that acts as a context-aware code retrieval broker for AI coding agents, enforcing backpressure and token budgeting to prevent context window overflow.

Indexes codebases into a SQLite database to provide metadata, exports, dependency graphs, and change tracking for JS/TS projects. It enables users to search for symbols, map internal dependencies, and monitor file changes through MCP tools and a web dashboard.

Enables LLMs to efficiently read, write, and refactor code using precise AST-based operations, reducing token usage and context window waste.

Enables coding agents to retrieve token-budgeted, source-hashed code context from explicitly registered local repositories via read-only MCP tools, reducing broad repository crawling while providing structure, symbols, and impact slices.

A local MCP server for AI coding agents that builds a queryable knowledge graph of files and symbols, enforces edit scope before file writes, and records append-only reasoning logs to explain changes.

Local code context engine for Codex App/CLI that indexes code with SQLite/sqlite-vec for hybrid search and context packing to provide relevant files, line numbers, snippets, and paths.

Enables coding agents to reduce context tokens by requesting file outlines, individual symbols, compact searches, and condensed test, lint, and type-check failures instead of reading whole files or raw command output.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

Read-only repository context explorer for coding agents. Provides repository exploration tools via CLI or MCP adapter.

Graph RAG based context management engine for coding agents

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

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.

Enables AI coding agents to perform zero-token static audits, simulate refactorings in an ephemeral RAM clone, trace downstream ripple effects, and block unsafe writes with automatic rollback to prevent regressions.

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.

MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Provides VS Code active editor context (open file, cursor position, selection) to MCP-compatible AI tools.

Enables AI coding assistants to automatically scan, store, and query API endpoints from codebases, providing instant lookup and semantic search to reduce context switching and token consumption.

Enables AI coding agents to retrieve budgeted, AST-aware edit context and run refactor-safety checks over a local Tree-sitter code knowledge graph, reducing token use by avoiding full-file reads.

Enables users to compress text, extract Python code signatures, hydrate saved session state, and save checkpoints, reducing token usage when handing context between models and agents.

Enables AI coding assistants to retrieve a lean, optimized codebase context without lockfile and build bloat, and to analyze which files consume the most tokens.

MCP server for semantic code search and dependency graph analysis. Indexes codebases into a knowledge graph with vector embeddings for AI-powered code understanding.

Context Bridge is a lightweight MCP server that builds a persistent semantic knowledge graph of your codebase, enabling AI assistants to query complex codebases with sub-millisecond latency without re-reading files every session.

A local code-intelligence engine for AI agents that indexes repositories into a PostgreSQL-backed code graph and serves structured, token-budgeted context over MCP and HTTP, enabling targeted queries on symbols, dependencies, contracts, and impact analysis.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

Enables AI coding agents and CI workflows to audit instruction files, skill definitions, MCP configurations, tool schemas, and prompt-cache call sites for broken references, token-budget drift, security hazards, and payload waste. It surfaces structured findings so teams can enforce context-layer health before changes ship.

Enables LLMs to progressively explore a project's dependency graph and retrieve only relevant code through a three-layer MCP workflow, with AST skeletons, Prisma schema extraction, token budgeting, and secret redaction.

Make any LLM a codebase expert instantly. Provides deep code intelligence through semantic search, architecture mapping, security analysis, and smart context that fits perfectly in token windows.

Provides AI agents with secure, read-only file system access to analyze and understand project codebases, enabling multi-repository context aggregation and cross-project code tracing.