"CopilotKit - AI copilot development framework" matching MCP servers:

MCP server for financial due diligence that enables company lookup, financial ratio calculation, and document search over corporate filings.

A middleware system that connects large language models (LLMs) with various tool services through an OpenAI-compatible API, enabling enhanced AI assistant capabilities with features like file operations, web browsing, and database management.

Integrates Microsoft 365 enterprise data into GitHub Copilot and Claude Desktop using Microsoft 365 Copilot APIs. It enables natural language queries across SharePoint, OneDrive, emails, calendars, and Teams meetings while maintaining full enterprise permission enforcement.

Enables any MCP client to run a complete data-science pipeline on a CSV: exploration, cleaning, model comparison, training, tuning, and plain-language analysis. Exposes nine tools that turn raw data into trained models and reports.

Enables MCP-based hybrid semantic code search over repositories, providing tools for searching code by meaning or keyword, retrieving symbol definitions, finding usages, reading chunks, and listing indexed repos.

A local MCP server that provides persistent, project-scoped semantic memory for GitHub Copilot in VS Code, using ChromaDB and embeddings for RAG-based memory retrieval across sessions.

Enables conversational triage of ServiceNow incidents via Claude Desktop, automatically classifying and routing them based on company knowledge.

Exposes code search and file reading tools over the Model Context Protocol, enabling any MCP-compatible client to query a codebase with natural language.

Enables AI coding agents to search a security corpus, scan Terraform plan JSON for cited findings, and list sources via MCP tools grounded in CIS AWS Foundations and OWASP Top 10 CI/CD risks.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Ingests pdf and epub documents for LLM-retrieval (RAG)

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

A simple AI development tool that helps users interact with AI through natural language commands, offering 29 tools across thinking, memory, browser, code quality, planning, and time management capabilities.

Interact with WebScraping.AI API for web data extraction and scraping

Open-source AI memory layer for LLM agents. Importance scoring, temporal decay, hierarchical memory (facts, summaries, themes), YMYL prioritization, and active retrieval with contradiction detection. Supports OpenAI, Anthropic, Ollama. Local-first with SQLite + FAISS.

MCP server that enables AI-powered content generation in multiple formats using Google Gemini and Tavily real-time research, accessible via MCP-compatible clients.

Enables AI coding agents to persist and retrieve project knowledge using three MCP tools with local vector and full-text search.

Persistent memory for any AI assistant. Zero token cost until recall. Stores memories in local SQLite, ranks by 6-factor scoring, returns results 79% smaller than JSON. Works with Claude, ChatGPT, Grok, Cursor, Windsurf, and any MCP client.

MCP server for semantic research: search arXiv, fetch papers, and answer questions grounded in the actual paper text via RAG tools, resources, and prompts.

Enables searching, indexing, and recovering past AI chat sessions and agent actions from local AI coding tools, with optional vector database semantic search and direct filesystem access.