"How to create charts from data" matching MCP servers:

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

An MCP server that runs Apify scraping actors with quality-gated validation, scoring, and retries, returning embedding-ready chunks for AI agents.

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Enables LLMs to query structured statistical data from the Swiss Federal Archives' Linked Data platform (LINDAS) by translating natural language questions into SPARQL queries against RDF data cubes.

DC Hub is the live Model Context Protocol layer for data-center and energy infrastructure. AI agents call 92 tools across 24,600+ facilities for land+power, grid, interconnection, fiber, and Capacity Source — with as_of timestamps. Connect: https://dchub.cloud/connect · Docs: https://dchub.cloud/ai-agents · Endpoint: https://dchub.cloud/mcp · Pro $99/mo.

Powers AI agents with indexed blockchain data from The Graph, enabling them to fetch subgraph schemas and execute GraphQL queries against blockchain data.

Secure Edge Gateway for Data Normalization.

AI-powered deep research agent with multi-source search (DuckDuckGo, Google News, Reddit, Wikipedia), structured analysis (SWOT, comparisons, timelines, Google Trends), financial data with charts, and PDF report generation. 11 MCP tools. Multi-LLM support (DeepSeek, Gemini, GLM, OpenAI).

A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.

Effortlessly create clean, elegant links in bulk — powered by AI.

Enables LLM agents to query a contract data room using structured metadata filters and BM25 full-text search over contract articles, returning relevant contracts, justifying clauses, and explicitly excluded unknowns. It supports exhaustive list answers and citation-ready JSON for MCP clients.

A Model Context Protocol server for Chroma, enabling AI models to create collections and retrieve data using vector search, full text search, and metadata filtering.

A server that provides AgentQL's data extraction capabilities enabling AI agents to get structured data from unstructured web

MCP server for the BAIC Data Layer. Provides tools to manage sources, knowledge bases, knowledge clusters, run data agents, and create/manage pipelines.

Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

An MCP server implementation that enables interaction with the Unstructured API, providing tools to list, create, update, and manage sources, destinations, and workflows.

Enables LLMs to create, query, and manage MongoDB-compatible databases using natural language without actual data storage. Uses statistical modeling to generate realistic data on-the-fly from sample documents or descriptions.

Connect your Sanity content to AI agents. Create, update, and explore structured content using Claude, Cursor, and VS Code via the Model Context Protocol. Transform content operations from complex queries to simple conversations—giving your team superpowers without sacrificing structure.