"A search engine platform" matching MCP servers:

一个基于Model Context Protocol的SQLite数据库查询服务，为AI应用提供标准化的数据库访问、安全的SQL查询执行以及表结构预览功能。

Give your AI agent the entire China A-share market: 55 datasets of prices, financials, capital flows, corporate events and futures/options, stored locally and queryable with read-only SQL. Built point-in-time with delisted stocks kept, so backtests don't lie — no account, no API key.

Read-only MCP access to the p-e relay store: an append-only corpus of provenance-bearing coordination records, with tools to fetch a record by id, list records and replies, and check existence. This image serves a snapshot frozen at build time; append_relay is listed but refused, and deposits to the live store require an HMAC-signed request.

Enables MCP clients to securely query databases, list tables, inspect table schemas, and test connections across Oracle, SQL Server, MySQL, PostgreSQL, and SQLite using read-only SQL tools.

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.

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

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.

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.

Build and run a complete backend from your agent: relational data with row-/field-level security, full-text + semantic + geo search, RBAC and BYOK, a workflow/automation engine, a growth & retention engine (email, push, promotions, per-user referral codes, rewards and points), payments and marketplace billing, and a growing catalog of integrations – the whole Anythink platform as one CLI-backed MC

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.

A Model Context Protocol server that exposes Firebase Firestore and Authentication to AI agents, supporting multi-project configurations and read operations.

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 MCP hosts like ChatGPT and Claude to query data platform metadata, explore schemas and tables, inspect SQL explain plans, and monitor DAG and ETL status through standardized tools.

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.

Provides an MCP protocol interface for interacting with Elasticsearch 7.x databases, supporting comprehensive search functionality including aggregations, highlighting, and sorting.

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

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

Equips AI agents with tools to validate Pydantic document models, search architectural patterns, deploy project scaffolding, retrieve blueprints and manuals, and generate repository CRUD classes for a synchronous/asynchronous MongoDB engine.