"Connecting external data sources for contextual LLM calls" matching MCP servers:

Persistent memory for AI coding agents: local-first semantic search, dependency graphs, git history.

A Model Context Protocol server that provides Retrieval-Augmented Generation capabilities using Contextual AI, enabling AI interfaces like Cursor IDE and Claude Desktop to query domain-specific knowledge with context-aware responses and source citations.

Provides RAG (Retrieval-Augmented Generation) capabilities via Contextual AI, enabling query processing and context-aware responses with citations. Integrates with MCP clients like Cursor IDE and Claude Desktop.

Gives LLMs persistent, searchable semantic memory, with tools to store, search, retrieve, update, delete, and list memories across configurable embedding backends.

Enables an LLM to autonomously maintain a Markdown-based wiki with YAML frontmatter, using hybrid BM25 and local CPU vector search for fully offline retrieval without external APIs or databases.

Integrates Dify's external knowledge base API with the Model Context Protocol to enable AI agents to retrieve and query relevant information. It supports relevance scoring, metadata filtering, and flexible configuration through environment variables or command-line arguments.

Share code context with LLMs via MCP or clipboard

MCP server that enables persistent, hybrid, local memory for LLM agents, with vector + BM25 search, knowledge graph, and policy-driven retention, providing token-budgeted context injection for AI assistants.

MCP server for grounded, cited AI: answers questions from live web sources, verifies claims, fact-checks documents, searches and reads URLs, summarises, classifies, and extracts fields, with usage tracking and status.

Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.

Enables AI agents and MCP clients to compress retrieved web documents against a query, pruning irrelevant noise through deterministic algorithmic scoring so downstream context stays lean. It exposes this capability over the Model Context Protocol for integration with Claude Desktop, Cursor, and similar clients.

Enables AI agents to compress retrieved web documents against a query, pruning irrelevant noise so only the most relevant context reaches the model. It runs as a zero-dependency Python MCP server that accepts a query payload and returns structured, scored output.

Give your AI assistant real-time LLM/VLM knowledge. Pricing, benchmarks, and recommendations — updated every hour, not every training cycle.

Enables natural language query and ingestion of wiki knowledge bases built from documents, powered by Claude Agent SDK skills.

A Model Context Protocol server that provides unified access to multiple LLM APIs including ChatGPT, Claude, and DeepSeek, allowing users to call different LLMs from MCP-compatible clients and combine their responses.

An MCP server providing RAG and CAG capabilities for querying and augmenting BambiSleep knowledge base data.

An MCP server that routes LLM requests across multiple providers and orchestrates other MCP servers, with a focus on local privacy for embeddings and memory.

Evaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.

Enables AI agents to query structured data refined from unstructured web sources, including developer breaking changes, B2B pricing matrices, regulatory compliance, semantic search, and on-demand URL refinement.

Enables LLM clients to read, search, create, update, and delete notes in a self-hosted shared memory bank over MCP, using hybrid vector and full-text search with optional summarization.