"A guide for reducing token count in AI requests" matching MCP servers:

An MCP server for the tracking-requests shipping-label app, providing a broad read-only database surface and a staged write path for FedEx label creation.

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 tools to understand a database, inspect schema, and run safe SELECT queries with SQL guardrails, plus optional codebase reading.

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 modular monolith MCP server connecting multiple providers (Redash, PostgreSQL, GitHub) with unified permission, security, and auditing, enabling secure data access and tool execution.

A safety-gated Python MCP server for Metabase that lets MCP clients and agents inspect Metabase, run governed queries, and create or maintain dashboards, cards/questions, collections, snippets, permissions, and other Metabase assets through the Metabase REST API.

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.

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 natural language data queries with statistical rigor and a semantic layer, supporting 23+ analytical tools for product analytics.

MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.

Universal database copilot for diagnostics, operations, and performance analysis via MCP and CLI.

Enables exploring Microsoft Fabric / Power BI workspaces and semantic models, and executing ad-hoc DAX queries.

Enables AI agents to conversationally interact with genomics research networks for data analysis and discovery across multiple Omics AI Explorer platforms. It provides tools for exploring data collections, examining table schemas, and executing SQL queries against datasets like Viral AI and Neuroscience AI.

Minimizes LLM token consumption via YAML serialization, HTML-to-Markdown distillation, and pre-flight token counting, with tools for web content extraction and metrics database querying.

MCP server that allows Claude AI to interact directly with MySQL databases, enabling query execution and table information retrieval through natural language.

Enables AI assistants to use Neo4j knowledge graphs and Qdrant vector databases for hybrid reasoning, combining structured facts with semantic search for advanced knowledge management, research analysis, and standardized coding workflows.

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.

Enables AI clients to securely access and query Microsoft Dynamics 365 data through natural language, supporting data analysis, reporting, and business workflows.

A production-style MCP server exposing six business tools (web search, database, CRM, email, calendar, analytics) to AI clients via a secure, authenticated interface, with mock mode and a reference agent client.

Enables AI clients to securely interact with Oracle databases using natural-language prompts, translating requests into structured queries and returning results for AI-powered data access.