"Bitcoin analysis and information" matching MCP servers:

Integrates Jina.ai's Grounding API with LLMs for real-time, fact-based web content grounding and analysis, enhancing LLM responses with precise, verified information.

MCP server that parses aviation accident reports (PDF/DOCX/TXT) and injects their content into extraction prompt templates for knowledge graph construction.

Enables real-time web search with source citations by leveraging Perplexity AI's Sonar models through SAP AI Core. It integrates with Claude Code to provide deep analysis, reasoning, and up-to-date information from the web.

A two-sided marketplace that enables AI agents to buy and sell datasets, prompt packs, and MCP tools using Bitcoin Lightning for instant settlement. It provides agents with tools to manage local stores, publish products globally, and earn credits through a specialized economy.

Enables AI-powered medical information retrieval through FHIR clinical document search and GraphRAG-based exploration of medical entities and relationships. Combines vector search with knowledge graph queries for comprehensive healthcare data analysis.

Enables MCP-capable assistants to answer questions about Bitcoin SV from a pinned, auditable snapshot of specifications, SDKs, opcodes, and related documents, with read-only tools for investigation, search, and dependency checks.

Enables AI assistants to perform real-time internet searches and get AI-powered analysis of current information through the gemini-cli tool.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

Provides AI assistants with up-to-date legal documents from official sources, enabling accurate legal information retrieval and analysis.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

A PostgreSQL MCP server that automatically detects and obfuscates personally identifiable information (PII) in query results using column-name heuristics and NLP analysis. It enables AI agents to interact securely with databases by masking sensitive data by default while allowing selective unmasking under user control.

A Python-based FastMCP server that provides financial tools for securities analysis, including market data, news, fundamental/technical analysis, and visualization capabilities that can be consumed by any MCP-aware client.

An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.

Provides web and local search capabilities via Brave Search combined with advanced academic paper analysis using Google's Gemini model. It allows AI assistants to perform real-time information retrieval and structured research synthesis directly through the Model Context Protocol.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Enables AI assistants to analyze B2C Gold IRA sales call transcripts through 6 specialized prompts executed in strategic order. Provides structured analysis of conversation dynamics, psychology, objections, deal risk, and strategic qualification for retirement planning sales calls.

A Model Context Protocol server that provides document analysis capabilities to LLM applications, including extraction, chunking, summarization, and semantic search for PDF, DOCX, and plaintext documents.

An MCP server that provides targeted PostgreSQL schema access to AI agents, retrieving only relevant tables for a question and enforcing read-only access through multiple layers.

The open-source, self-organizing memory for all your AI tools. Persistent memory over MCP (remember, recall, observe): background agents extract entities, resolve conflicts, and keep cited syntheses current in an append-only, encrypted, single-tenant vault.

Enables saving project facts as they occur and retrieving only task-relevant entries when starting new work, using Jev evaluation to rank them while preserving all stored context.