"Optimizing AI Model Thinking, Token Usage, and Context Size" matching MCP servers:

MCP server enabling real-time weather queries via Tavily API and internet usage data by country via MongoDB.

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

MCP server for exploring and querying Supabase databases with interactive React widgets for schema browsing, table viewing, and SQL execution.

A secure multi-tenant server that provides real-time context to LLMs via REST API, using JWT authentication and PostgreSQL row-level security for tenant isolation.

Enables agents to query governed metrics through MCP, including describing metrics, running deterministic SQL against a warehouse, explaining join path preferences, and searching semantically ambiguous terms.

MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Enables agents to query a local macOS archive of Knowledge and browser history through read-only stdio MCP tools for status, timelines, search, original records/blobs, schemas, bounded SQL, and usage or domain summaries.

MCP server for connecting to Microsoft Fabric and Power BI semantic models, enabling workspace browsing, dataset management, TMSL retrieval, DAX queries, model creation/editing, and includes a Best Practice Analyzer with 71 rules.

MCP server for reading and analyzing Fabric semantic models. Supports getting model definitions and executing DAX queries against Power BI datasets.

Enables natural-language sales queries against a SQLite database, generating and executing read-only SQL through a secure MCP server with table listing, schema description, and query execution.

A middleware server that intelligently routes AI model queries to appropriate data sources, providing contextual information to enhance AI responses.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

Allows AI assistants to interact with Square's connect API, providing access to Square's complete API ecosystem for managing payments, orders, customers, inventory, and more.

Indexes codebases into a SQLite database to provide metadata, exports, dependency graphs, and change tracking for JS/TS projects. It enables users to search for symbols, map internal dependencies, and monitor file changes through MCP tools and a web dashboard.

A Model Context Protocol (MCP) server for developing Django applications. It exposes Django project information through MCP tools, enabling AI assistants to better understand and interact with Django codebases.

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 discover and invoke CRUD tools and read-only resources over the Model Context Protocol for generated API services.

Enables agents to pull compact MySQL row diffs into their context, supporting before/after changes via triggers or watermark-based updates, with tools to fetch and acknowledge changes.

Enables MCP clients to retrieve relevant memories and save confirmed durable facts through authenticated access backed by PostgreSQL provenance and Qdrant semantic search.

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

A memory management system that enables AI assistants to store, search, and visualize persistent conversation contexts using a Neo4j graph database. It provides an MCP server for integration with Claude Desktop along with a web-based dashboard for managing relationship-based knowledge.

A powerful MCP server that extracts, stores, searches, and analyzes Claude.ai conversations with SQLite database, semantic search, and multiple export formats including Obsidian and PDF.

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.

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.

CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.

Enables AI agents to inspect supported databases read-only, export sanitized DDL and metadata plus bounded masked samples into organized context, and run approved procedure or function updates through a shared local service.

Enables chat, document search (RAG), and a declarative SQL tool builder over MCP so any client can use these capabilities.