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"IBEX 35 Spanish stock market index information" matching MCP servers:

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    A local-first semantic search server for documents, supporting PDFs, Office files, and text/markdown, enabling natural language search via the Model Context Protocol (MCP).
    Last updated
    1
    MIT
  • A
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    Enables semantic search across your codebase using Google's Gemini embeddings and Qdrant Cloud vector storage. Supports 15+ programming languages with smart code chunking and real-time file change monitoring.
    Last updated
    15
    19
    MIT
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    Enables RAG-based querying of local stock company data using a local LLM and vector database, providing tools to ask questions, search raw chunks, and list documents.
    Last updated
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    Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.
    Last updated
    3
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    An MCP server that provides AI assistants with access to Multi Theft Auto: San Andreas function documentation through vector similarity search and smart keyword expansion. It enables efficient information retrieval with features like deprecation warnings and SQLite caching for technical documentation.
    Last updated
    11
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    GPL 3.0
  • A
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    Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.
    Last updated
    6
    3
    MIT
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    An MCP server that provides AI assistants with persistent, semantic memory using Turso for storage and OpenAI for vector search. It enables natural language operations to store, retrieve, and refine information with automatic duplicate detection and quality validation.
    Last updated
    5
    4
    MIT
  • F
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    A Node.js-based MCP server that enables AI agents to generate embeddings, index documents, and perform semantic vector searches using OpenAI and Chroma. It facilitates the creation of retrieval-augmented generation (RAG) pipelines for internal knowledge assistants and document-based workflows.
    Last updated
    3
  • A
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    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.
    Last updated
    5
    18
    MIT
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    Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.
    Last updated
    68
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    Enables AI agents to interact with INFINI Easysearch (compatible with Elasticsearch/OpenSearch APIs) through 121 tools covering cluster management, index operations, document manipulation, search queries, snapshots, and monitoring.
    Last updated
    100
    4
    MIT