"Cursor AI SDK and related codebase information" matching MCP servers:

Enables managing Databricks directly from Cursor IDE with 30 tools for notebooks, clusters, jobs, DBFS, SQL, and workspace operations.

A Model Context Protocol server that enables AI assistants to explore and interact with Cursor IDE's SQLite databases, providing access to project data, chat history, and composer information.

Enables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.

Enables AI assistants to manage Backendless applications, including databases, users, files, and API services, through natural language commands, with support for OAuth2 authentication and hosted endpoints.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Enables querying, searching, and analyzing Cursor IDE conversation history from SQLite workspaceStorage databases. Supports exporting chat data in multiple formats and provides workspace utilities for managing conversation data across projects.

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.

Search the fastest-growing US horse boarding directory: 6,000 facilities and 3,800 trainers across 48 states, plus boarding price data by state.

Enables LLM clients such as Cursor and Claude to discover and use generated REST services through MCP tools for create, update, and delete actions and MCP resources for list and get-by-id context. Services generated in one command are exposed simultaneously as REST endpoints and MCP tools and resources.

Enables AI assistants to interact with generated REST APIs by discovering and using CRUD tools and read-only data resources.

Enables AI assistants to interact directly with generated REST APIs by exposing CRUD operations as MCP tools and read-only data as resources.

Exposes generated CRUD services to LLM clients over the Model Context Protocol, providing tools for create, update, and delete actions plus read-only resources for listing and fetching records by id. It lets assistants like Claude, ChatGPT, and Cursor discover and manipulate the same data served by the project's REST endpoints without any extra glue code.

Exposes every generated CRUD service to LLM clients such as Claude, ChatGPT, or Cursor through discoverable MCP tools for create, update, and list actions plus read-only resources, so models can query and manipulate data without raw HTTP calls. It runs alongside the same codebase's REST endpoints, letting clients and AI share one data layer.

Enables LLMs to discover and invoke generated CRUD operations as MCP tools and read data through MCP resources, alongside standard REST endpoints, using one codebase.

Enables LLMs to discover and invoke generated CRUD tools and resources, allowing AI assistants to interact with the same data exposed by the REST API. Supports MCP clients such as Cursor and Claude over HTTP.

Enables LLMs to discover and invoke generated API actions as MCP tools and read context through MCP resources for list and get-by-id operations. It provides HTTP-based MCP access for clients such as Cursor and Claude without extra glue code.

Enables LLMs and AI assistants to discover and use tools and resources for CRUD operations on generated services, providing direct interaction with the same data exposed by REST endpoints.

Enables governed interaction with synthetic people data through MCP tools, providing per-caller scoped access, semantic-layer metrics, and read-only SQL with authorization checks.

Enables LLMs to interact with generated REST API services by exposing MCP tools and resources for CRUD operations, listing, and retrieval. It lets AI clients discover and call create, update, delete, list, and get-by-id actions against the same data available over REST.

Exposes every generated Express service to LLM clients over HTTP as MCP tools (create, update, delete actions) and resources (list and get-by-id context), so AI assistants can discover and manipulate application data without raw HTTP calls. This lets teams run a single codebase that serves both traditional REST clients and AI agents such as Cursor or Claude.