"Firebase Firestore Database Information" matching MCP servers:

Enables AI agents to format SQL, explain queries in plain English, analyze schemas, build queries from natural language, and generate migrations, all without requiring a database connection.

Offline PostgreSQL diagnostics: reviews SQL, risky migrations, candidate indexes and EXPLAIN JSON plans without database credentials or executing statements.

Enables AI agents to run deterministic database intelligence tasks including natural-language-to-SQL synthesis, AST SQL auditing and optimization, schema migration diffing, SQL transpilation, index and normalization advice, PII scrubbing, fixture generation, connection-pool sizing, isolation-hazard auditing, and ORM model compilation over Streamable HTTP or stdio.

Enables secure read-only querying of multiple database types (MySQL, PostgreSQL, MSSQL, Oracle) through natural language. Automatically reads database configuration from project files and blocks any data modification operations for safety.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

Enables AI coding assistants to scan projects for security issues such as leaked API keys, missing Supabase RLS, open Firebase rules, unauthenticated routes, and hallucinated packages, then fix and verify the results.

MCP server that provides npm package information for AI agents during TypeScript development.

Enables MCP-compatible AI clients to analyze Android APK and iOS IPA files for security issues through natural language, including permission auditing, API endpoint extraction, hardcoded secret detection, Firebase inspection, and third-party SDK enumeration.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

Enables AI editors such as Cursor, Claude Code, Windsurf and VS Code to scan a project for common AI-introduced security mistakes — leaked keys, open Supabase/Firebase data, missing auth, unverified Stripe webhooks, SQL injection and more — and return findings with exact fix instructions. It also provides a guided prompt that walks an agent through fixing critical issues one by one and verifying each fix by re-scanning, without ever editing files or rotating keys itself.

A comprehensive MCP server that provides a graph database for tracking software codebase components, their relationships, and associated tasks/goals.

Enables agents to query TypeScript and JavaScript code intelligence—type diagnostics after edits, go-to-definition, references, hover information, and document symbols—through MCP tools.

Indexes Unity projects into a SQLite database and exposes 23 MCP tools for Claude to explore scenes, prefabs, scripts, and assets via structured queries.

Enables AI agents to architect, validate, scaffold, and generate code for WTinyDB document database models and patterns.

Semantic search server for code and documentation using Qdrant vector database. Supports multi-language indexing, live updates, and natural language queries.

Headless MCP server for IDA Pro enabling on-demand database loading and programmatic reverse engineering workflows via MCP tools.

Enables AI agents to safely inspect Oracle Database schemas, objects, and PL/SQL source with a strict read-only guard, without ever modifying data.

Enables AI coding agents to inspect Runable projects over MCP, providing read-only access to project information and configuration via Runable's inspector.

140+ T-SQL static code analysis rules for SQL Database Projects

Enables querying a Neo4j knowledge graph built from Git repositories and relational database schemas via MCP, with tools for schema inspection, Cypher queries, and searching code and database entities.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.

Enables comprehensive MySQL database management including CRUD operations, schema queries, and natural language to SQL conversion support through complete database structure analysis.

Analyzes TypeORM entity source files offline to produce schema graphs, ERD diagrams, Markdown documentation, and missing-documentation audits. Enables AI clients to understand database schema without a database connection.

A hybrid search MCP that enables users to find coding chunks like RAG without indexing their codebase, without any vector database. World's first context attention MCP. It gives results in below 5 seconds in a repository as big as VS Code.

An MCP server for EndstoneMC development, enabling module information queries, code search, plugin template generation, event handling guidance, and development tutorials through natural language.

Analyzes SQL queries for performance issues, provides optimization suggestions with automated rewriting, and recommends indexes across multiple database dialects (PostgreSQL, MySQL, Oracle, SQL Server).

Enables AI assistants to index and search codebases using semantic search powered by multiple embedding providers (OpenAI, VoyageAI, Gemini, Ollama) and vector database storage.

Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.

Enables agents to query information about gems in a Ruby project's Gemfile, including source code and metadata.

An MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.