"A server for fetching data" matching MCP servers:

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

Enables persistent, auditable business operations state by storing knowledge, evidence, decisions, tasks, execution receipts, and review results in a local SQLite database, so different sessions can continue the same project.

Enables querying and retrieving content from Confluence through CQL searches and page content fetching, allowing Claude to seamlessly access information stored in Confluence workspaces.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

Enables AI agents to query and modify RDF data, list named graphs, and access pre-built SPARQL templates on a Fuseki server, with optional HTTP Basic authentication.

A persistent memory layer for AI tools that decouples personal data from AI's unstable memory, enabling you to mention information once and have it remembered forever across all conversations.

Look up countries, timezones, periodic table elements, physical constants, units, HTTP status codes, and MIME types via MCP. STDIO or Streamable HTTP.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A modular MCP server that provides file search, URL fetching, text reading, knowledge base search, and image embedding tools, with a web interface powered by an Ollama agent.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Schematica MCP Server provides 8 tools for AI agents to browse, search, and manage a curated library of reusable data model schemas, enabling standardized entity and relation type definitions for knowledge graphs.

Provides a semantic memory layer that integrates LLMs with OpenSearch, enabling storage and retrieval of memories within the OpenSearch engine.

Enables searching and fetching Wikidata entities, executing SPARQL queries, and resolving external identifiers via the Model Context Protocol.

Provides structured tool access to Mario's portfolio data including skills, projects, experience, and services via 7 MCP tools.

MCP server for enabling data extraction and context retrieval for agents, storing information in a knowledge graph using Amazon Neptune or FalkorDB.

A bridge between MCP Host applications and mem0 cloud service, specialized for project management with capabilities to store, retrieve, and search project information within a structured format.

A hierarchical MCP server for managing skill definitions with a browsable tree structure and full-text search. It allows AI agents to efficiently discover and use skills without consuming context tokens.

Structures debugging processes into a persistent knowledge graph, enabling problem decomposition, hypothesis testing, and reusable solution discovery.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.