"IBM MCP Context Forge - Model Context Protocol Framework" matching MCP servers:

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

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.

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.

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.

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.

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.

Enables repository context packing, file/symbol ranking, task-relevant selection, and applying structured patches via MCP.

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.

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

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.

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.

Provides an MCP server for AI coding agents to fetch AST-slimmed source files that keep critical logic like try/catch, auth, and Eloquent relations, cutting context tokens by 50%+.

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.

Enables coding agents to query a local, versioned knowledge graph of a software project, retrieving overviews, context packs, evidence, and explanations to make informed changes.

Loads entire codebase into LLM context for AI-assisted code understanding and editing.

Provides real structural understanding of a codebase via tree-sitter AST parsing before starting a task, reducing token waste and grounding acceptance criteria in code structure.

MCP server that validates AI-generated 1C:Enterprise (BSL) code against the real platform API. Catches unknown enum values, wrong argument counts, and missing type members by parsing the platform syntax-helper (shcntx_ru.hbk) — independent Rust implementation with built-in expression validator.

A pipeline that segments source code into chunks, generates embeddings, and stores them in ChromaDB. It provides semantic context to AI coding assistants via the Model Context Protocol.