"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.

Read-only MCP server exposing homelab data sources (files, mail, calls) to Cloudflare OS, enabling read-only access to documents, emails, and call records.

Enables agents to detect and redact PII and secrets in text, HTML, and PDF documents before they reach an LLM, with reversible placeholder, mask, or hash redaction, and to list supported detectors and redaction styles.

Bridges Codex to external CLI coding agents for read-only code analysis and deterministic quality fixes.

Enables AI agents to query free public APIs — SEC EDGAR, World Bank, GDELT, DATA.GOV.HK, HKMA, the official MCP Registry, UK Companies House, FRED and Jina Reader — as zero-dependency MCP tools. Every result is wrapped in a { source, retrievedAt, data } provenance envelope so agents can cite the origin and time of each fact.

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.

Enables any AI to place real phone calls in Spain and Mexico with an AI voice agent, then retrieve call status, summaries, collected data, and transcripts.

A local-first persistent contextual-memory engine with an MCP adapter for AI models such as Qwen.

Routes work from your coding agent across 20+ free-tier LLMs to cut costs 10-100x using a round-robin dispatch pool with cooldown-aware auto-fallback.

Enables MCP-compatible assistants to securely access external systems like Slack through permission-scoped, idempotent tools with tenant isolation, delegated OAuth consent, and an immutable audit trail.

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

Enables external reconnaissance activities including DNS enumeration, subdomain discovery, email security analysis, and SSL certificate inspection against a target domain.

Blind multi-model councils with anonymous LLM seats and direct 1:1 chat as a local MCP server. Enables asking one question to get independent answers from multiple model CLIs, then blind scoring and reveal.

Enables a primary agent to offload bounded, high-volume analysis of explicitly selected workspace files to an auxiliary LLM through a single read-only tool, returning text, Markdown, JSON, or unified-diff output with token usage while retaining all planning, verification, and edits.

Enables coding agents to query the current free LLM model lineup mid-task, check what free models were added or removed since the last scan, and look up curated provider free tiers with limits and caveats, so they stop hard-coding model IDs that silently break.

This MCP server enables MCP clients such as Claude Desktop to run LLM-generated code in a secure, isolated sandbox across multiple container backends and programming languages.

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.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

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.