"Figma Context MCP - Searching for Related Information or Tools" matching MCP servers:

Enables Cursor to access Figma files through the Model Context Protocol, enhancing its ability to accurately interpret and utilize design data for code generation.

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.

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.

Local MCP server that provides semantic search (RAG) over code repositories, enabling AI clients like Claude and Gemini to access project context without manual re-upload.

Local MCP server that transforms Figma documentation and business rules into a semantically searchable index, exposed as a tool for Claude Code to query via natural language.

Fantasy Premier League stats and press coverage for AI agents: read-only SQL over FPL player, fixture and gameweek data, plus semantic search over BBC Sport and Guardian articles and FPL injury news. Bring your own PostgreSQL and Pinecone, with bundled ingestion jobs.

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.

Enables semantic search across documentation stored in Gemini FileSearchStores, returning AI-generated answers with source citations.

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.

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.

Provides an external context buffer so a local model can maintain session memory across long tasks and search and page through large documents without loading them whole into its limited context window.

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.

Give your AI agents access to 8,500+ community curated awesome lists with over 1 million curated resources.

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

An MCP server that enables processing of massive datasets up to 10M+ tokens using a recursive language model pattern for strategic chunking and analysis. It automates sub-queries and result aggregation using free local inference via Ollama or the Claude API to handle context beyond standard prompt limits.

A collection of tools that enhance MCP-based workflows with caching, retry logic, batch operations, and rate limiting capabilities.

Context Forge is a vendor-neutral continuity layer for AI tools. It stores project context in Markdown and JSON, retrieves task-scoped context, and leaves structured handoffs so another model can continue without being taught the project again.

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.

Enables local-first context retrieval and management from JSONL and Markdown sources through MCP, with a rebuildable full-text search index and support for stable reference pagination.

The URL-Context-MCP MCP Server provides a tool to analyze and summarize the content of URLs using Google Gemini's URL Context capability via the Gemini API. Now also supports optional grounding with Google Search alongside URL Context. The server is designed to follow prompt-only orchestration: con