"browser use" matching MCP servers:

Turns a tools.yaml contract into typed, tenant-guarded vector database tools that agents can use via Python imports or MCP.

Provides fast static embedding, similarity, and search capabilities via MCP tools and an OpenAI-compatible HTTP API using a tiny 16M-parameter English embedding model.

A focused local-paper RAG MCP server that imports PDFs, indexes them into SQLite and ChromaDB, and retrieves section-aware paper chunks for use with Claude Code, Codex, or any MCP client.

Enables Claude to index and retrieve context from codebases using self-hosted Milvus for semantic search, with hardened reliability and security for production use.

An archived, unsafe MCP server with critical security vulnerabilities, classified as a pump and dump operation. Do not use in production.

Pay-per-use semantic memory for AI agents with cryptographic attestation. Vector embeddings with SHA256 commitment, secp256k1 signature, and Lightning invoice.

Enables semantic search across browser bookmarks from Chrome, Firefox, Edge, Opera, and other browsers using natural language queries. Extracts and indexes bookmark content and metadata into a vector database for intelligent retrieval.

Enables AI agents to use a neuro-symbolic memory fabric with bi-temporal knowledge graph and holographic VSA, providing tools for adding, searching, temporal queries, auditing, and proving memories with cryptographic provenance and zero-LLM ingest.

Enables MCP-capable agents to retrieve relevant slices of a user-story knowledge graph (stored in Supabase + pgvector) using three retrieval verbs: find_related, find_crossover, and query_stories.

Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI Also