"Informational MCP Servers with Croissant Dataset Format Support" matching MCP servers:

Enables version-controlled golden dataset management and semantic evaluation for RAG/LLM pipelines using TF-IDF cosine similarity, without requiring an LLM API key.

Enables an ElevenLabs support agent to call support tools over MCP, including Discord ticket flows, post-call webhook handling, and knowledge-base syncing.

Exposes order status lookup and knowledge base search tools from the Support Agent AI over MCP, enabling MCP clients to handle customer support queries with grounded, citation-backed answers.

Enables collection, enhancement, and quality scoring of authentic Amharic datasets, with integration for AI models like Gemini and Qwen.

Exposes a support-ticket inbox as tools an AI client can use to list, inspect, triage, and update tickets, including LLM-based category, priority, and sentiment classification with a drafted first response informed by similar past tickets. It works with a mocked dataset styled after a home-loan partner-support inbox, with the tool interface designed so a real ticket source could be swapped in later.

Enables AI assistants to handle customer support by looking up order status, semantically searching knowledge bases, and creating support tickets through MCP tools, with grounded answers and automated evaluations.

Enables natural language querying and semantic search over a bilingual customer support ticket dataset using SQL and embeddings.

A local stdio MCP server that brings scattered tickets, transactions, and current policies together so a model can retrieve evidence, assess cases, and prepare reply and escalation drafts. All writes stay local and pending human review, with no ability to send messages, move money, change accounts, or publish content.

Enables MCP clients to list, create, and update support tickets, classify them by category, priority, and sentiment, detect duplicates, and answer queue questions using retrieval-augmented LLM tools.

Enables a support bot to answer FAQ questions from a local vector store, with admin tools to add, list, and delete knowledge entries while enforcing read-only versus admin bearer-token permissions.

Enables support engineers to analyze tickets against a help center with cited retrieval, role-gated and human-confirmed actions, and safety-gated evaluation.

Enables AI agents to check order statuses, retrieve FAQ policy answers via RAG, and create/manage support tickets, all based on the e-commerce support workflow in the repository.

Enables enterprise SaaS support teams to get retrieval-augmented answers from indexed support documentation and to run natural-language questions as SQL queries against a support PostgreSQL database. It returns answers with source references and exposes the generated SQL alongside its results, with optional tracing and evaluation.

A Model Context Protocol (MCP) server that provides AI-powered customer support using Cursor AI and Glama.ai integration.

Enables LLMs to perform sophisticated web searches through proxy servers using Tavily's API, supporting comprehensive web searches, direct question answering, and recent news article retrieval with AI-extracted content.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

Enables job-application lookup and domain support through MCP, exposing tools such as job lookup by ID over a local HTTP endpoint.

A Model Context Protocol server that exposes multiple AI tools over SSE transport with JWT-based secure authentication, allowing for dynamic tool registration and session management.

Enables ticket management through a REST API and support document search via a local vector database, powered by Ollama and LanceDB.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.

A Model Context Protocol server that enhances LLM capabilities by connecting to Wikipedia, internet search (Tavily), and financial data (Yahoo Finance) tools, enabling contextual responses to user queries.

Production-ready MCP server that integrates OpenAI API with extensible tool support, enabling dynamic plugin loading and knowledge search capabilities through multiple interfaces including CLI and browser UI.

Multi-user MCP gateway for WeKnora v0.7.1 that accepts per-request WeKnora API keys, enabling client-scoped access to spaces and knowledge bases through a single Streamable HTTP MCP endpoint.

Exposes read-only knowledge base articles on returns, warranty and shipping policies alongside tools that search, retrieve and create real support tickets. This lets an LLM agent ground its answers in store policy without inventing information, and act on actual ticket data rather than simulated records.

Provides authenticated access to PostgreSQL databases for Claude AI, enabling users to browse database tables, discover schemas, and execute custom SQL queries through natural language interaction.

Enables LLMs to search and analyze Swiss case law, legislation, and citation networks with 43 tools for decision search, statute lookup, citation graphs, legislative history, and exam question generation.

Implements a Model Control Protocol server integrated with Google Gemini LLM, providing a flexible framework for building AI-powered applications.

Enables hybrid search (BM25 + semantic with RRF) over documentation, with Thai language support. It exposes MCP tools for adding documents and searching via Qdrant and Ollama/OpenAI, using a stateless Streamable HTTP transport.

An MCP server that provides hybrid search over Confluent support articles and lets AI assistants save solved problems back into the knowledge base.