"A search engine that searches multiple other search engines" matching MCP servers:

Enables full-text search of macOS iMessages including link preview metadata. Works as an MCP server for Claude Desktop to search your messages locally.

MCP server that aggregates job listings from Remotive and Arbeitnow, with local SQLite caching, application tracking, and saved searches.

A Model Context Protocol server that searches transcript segments in a Turso database using vector similarity, allowing users to find relevant content by asking questions without generating new embeddings.

Downloads your entire Search Console dataset into a local SQLite database, then gives your LLM a pre-built SQL query library for every standard SEO analysis type, with context available for your LLM to perform any SQL query to answer your questions and analyse for you.

Enables AI systems to perform full-text and semantic search operations over structured/unstructured data in Azure Cognitive Search, with capabilities for document indexing and management through natural language.

Provides access to Naver Search APIs, allowing AI agents to search across multiple categories (blogs, news, books, images, shopping items, etc.) with structured responses optimized for LLM consumption.

Enables searching for any text within a specified Algolia index through an MCP-compatible interface. It allows users to integrate Algolia search capabilities into their environment using an application ID and API key.

A server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.

An MCP server that indexes local Python projects into a SQLite database to enable efficient symbol searching and dependency tracking. It allows users to find function or class definitions, trace module imports, and read file contents through natural language interfaces.

Enables interaction with MongoDB databases through CRUD operations, aggregation, and schema discovery, with automatic field validation and ObjectId conversion.

Enables AI agents to safely operate on PostgreSQL data through typed MCP tools with per-identity RBAC, guardrails, and immutable audit logging.

Enables AI agents to look up Chinese laws and statutory articles fully offline from a local SQLite database, resolving a law name plus article number to verbatim text, searching articles by keyword, and retrieving a law's chapter structure and version history. It also verifies citations in bulk, checking whether each quoted provision exists word-for-word and is currently in force.

This read-only MCP Server allows you to connect to Google Search data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

This MCP server provides secure access to databases for AI agents, enforcing authentication, authorization, human approval, logging, and notifications to prevent dangerous actions.

Enables AI assistants to query and manage a local Engine DJ library on macOS, including searching tracks by BPM, key, genre, or comments, auditing duplicates and missing files, reading cues and beatgrids, and building playlists or fixing tags when write access is enabled.

Connect AI assistants to Manticore Search. Execute SQL queries, list tables, get schemas, and fetch documentation. Perfect for building RAG applications and search-powered AI agents.

Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.

A provably read-only SQL layer for AI agents (MCP) and weekly reports — over the database behind an ERP (Logo Tiger, Netsis, Mikro). Statement-checking guard, measured against 28 attacks.

Enables natural-language-driven analytics over CSV/Parquet or built-in data by decomposing questions into analytical tasks, running them via deterministic DuckDB and SciPy tools, and publishing only claims verified against the underlying rows.

Enables unified access to SQL, semantic vector search, and columnar analytics through a single MCP interface.

Enables an event-driven operations agent to query internal SQL, GitHub, Slack, Jira, documentation, and Kubernetes telemetry through 28 RBAC-gated MCP tools, with shared memory for playbook retrieval and incident workflows.

An MCP server that exposes personal financial data — transaction ledger, portfolio holdings, live/historical market prices, and quantitative risk metrics — as standardized tools, resources, and prompts, enabling natural language reasoning over real computed numbers.

Enables agents to perform controlled enterprise data queries through semantic intent, with runtime validation of statistics, filters, granularity, permissions, and physical bindings. Exposes tools like semantic_query for safe, fail-closed access to data horizons and capabilities.

A Model Context Protocol server that provides tools for searching private GitHub repositories, enabling access to repository files, commit history, and issues with automatic handling of rate limiting and error handling.

Enables searching and retrieving job listings from a Supabase database (h.careers) with filters like location, level, salary, and work format.

Enables AI assistants to search, query, and manage Azure AI Search indexes using full-text, semantic, and vector search, alongside index and document operations.

Securely connects AI agents to multiple databases simultaneously while enabling collaborative learning from team query patterns, all while keeping data private by running locally.

Enables users to query the CVE-Search API to browse vendors and products, retrieve CVEs by ID, list vulnerabilities for vendor/product combinations, fetch recent CVEs with CAPEC/CWE/CPE expansions, and inspect database information.

Generates cryptographically secure, compliant UUIDs (v4, v5, v7) to prevent AI-hallucinated identifiers that would cause database collisions.

Enables querying the CVE-Search API to retrieve vendors, vendor-associated products, vulnerabilities by vendor/product or CVE ID, recently updated CVEs with CAPEC/CWE/CPE expansions, and database metadata.