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

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.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

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 providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

Intelligent context manager for AI coding assistants that uses a three-level memory system (core, active, archive) to remember project context across conversations.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

Enables MCP-capable clients to query coding-agent conversation exports and retrieve token-bounded evidence bundles with source provenance and hash verification, so LLMs can ground responses in verifiable history without replaying full transcripts.

Local-first memory for AI agents about the people in your life. MCP server + CLI on SQLite. Never phones home.

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

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.

Provides official INDEC register designs and methodological rules to AI models, enabling accurate EPH data analysis code (R/Python) without hallucinations.

Persistent project context (AGENTS.md), cross-session memory, and identity — discoverable by any MCP client.

Enables AI agents to parse PDFs and search local document context through multimodal extraction and vector-based retrieval, all exposed as MCP tools.