"Information on LinkedIn Business or LinkedIn Advertising" matching MCP servers:

Analyzes LinkedIn saved jobs with EROI scoring and writes analysis back to a knowledge base for automated reporting.

Enables querying company knowledge base using RAG, providing accurate answers from internal documents via MCP.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

An integrated advisory system that transforms Claude into a personal career coach, life strategist, and business consultant with persistent memory, accountability tracking, and multi-lens analysis.

An MCP server that enables AI assistants to query and access structured knowledge about German family businesses through a Neo4j graph database, supporting complex relationship queries and semantic searches.

Provides financial agents access to the Financial Industry Business Ontology (FIBO) for querying financial concepts, terms, and relationships through natural language.

Exposes Microsoft BCQuality repository (Business Central development best practices) as structured tools for AI agents, enabling rule discovery, search, and application via MCP protocol.

Reference implementation of the Operational Ontology pattern: MCP tools are generated from a typed business domain model (objects, links, actions) — one tool per query shape and per action, deliberately no raw SQL tool. Writes pass business-rule preconditions, are audited, and write back to the systems of record; refusals are machine-readable. Ships with a demo orders ontology spanning two legacy

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

Provides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.

Enables creating and querying semantic knowledge graphs to model business logic, code relationships, and project structure across multiple projects.

Enables AI agents to record, recall, correct, and forget evidence-backed factual claims with temporal history, while explaining whether remembered information is current, historical, or contested.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

A design knowledge MCP server that Claude can query when generating UI, covering eight layers: principles, patterns, content design systems, research methods, service design, brand/visual, business strategy, and design tokens.

Enables a local-first MBA recruiting coach inside Claude that remembers your campaign, tracking target firms, live job boards, deadlines, applications, connections, and warm intros, while drafting human-approved outreach and never submitting applications or scraping LinkedIn.

Enables building and managing knowledge workspaces with tools for ingesting content from text, clipboard, or URLs and searching organized knowledge bases. Supports multiple configurable workspaces with separate inbox and knowledge directories for structured information management.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

An MCP server that gives AI agents a private, durable memory of people, providing a personal people graph in PostgreSQL with tools to search, remember, and connect people, plus imports from LinkedIn, Google Contacts, and WhatsApp, and a browser UI.

Headless document processing for AI agents. Invoice extraction, contract analysis, and Dutch business verification. Pay-per-use via X402 on Solana. No API keys needed.

Enables interaction with the RAGFlow API to manage knowledge base datasets and conduct AI-driven chat sessions. Users can list datasets, create chat assistants, and retrieve or query information from specialized knowledge bases.