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

Enables access to Usage and Billing APIs for managing accounts, products, meters, plans, and usage reporting. Supports operations like creating products/plans, reporting usage, and retrieving billing information.

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.

Enables agents to calculate how many evaluation test cases are needed to trust model comparisons, find the smallest detectable accuracy gap, and assess paired or unpaired results using McNemar and two-proportion statistical tests.

Enables AI agents to compose MCP tool calls into Unix-style pipelines, executing complex workflows server-side and returning only the final result.

A production-ready MCP server template that connects LLMs and AI agents to external data, tools, and services with built-in OAuth 2.1 authentication, Redis-backed session management, and a modular tools engine.

A basic Python implementation of a Model Context Protocol server for educational purposes, using FastAPI and WebSockets.

MCP server for querying OpenAI usage and cost data, including spend summaries, daily breakdowns, month-over-month comparisons, and token usage by model.

AI coding usage analytics — track spend, model usage, and costs across Cursor and Claude Code sessions.

MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.

Provides AI assistants with structured access to metadata for 76+ AI models across Ollama, Claude, and OpenRouter, enabling capability queries, compatibility checks, model comparisons, and task-based recommendations.

Enables AI agents to discover, compare, and select the best AI models across multiple providers based on pricing, performance, and capabilities, with real-time cost estimation and benchmarking.

An MCP server that retrieves GitHub Copilot usage metrics and seat assignment data across Enterprise, Organization, and Team levels. It allows users to monitor code completions, chat activity, and active user counts through integrated tools.

A MCP server for tracking AI usage metrics and structured logs across applications. Monitor model calls, analyze usage patterns, track costs, and debug AI interactions.

Enables MCP clients to query project-scoped Graphify graphs, search, read, and write Obsidian Markdown notes, and recall or store persistent ICM memories over authenticated private HTTPS. It serializes access per project and supports slim and standard Docker deployments without embeddings or extra memory services.

Exposes GitHub Copilot premium request usage as an MCP tool, providing a breakdown by model and cost.

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

MCP server for real-time Solana token risk analysis. Cross-references RugCheck.xyz, DexScreener, and GoPlus Security to generate three-layer reports: machine verdict → LLM analysis → raw on-chain evidence. Live on Solana mainnet with USDC micropayments ($0.02/audit). Give any AI agent the ability to check if a token is safe before trading.

Enables any MCP client to surface twelve structured thinking frameworks for clarifying questions, learning, problem-solving, decision-making, and self-reflection, with fillable slash commands for humans and programmatic retrieval for agents.

Persistent sequential thinking for MCP clients, stored durably on Cloudflare Workers with support for branching, revision, and history retrieval.

Enables Claude to read your Patch API budget usage, including spend, burn rate, and account details, directly from chat.

A powerful MCP server that enhances LLMs with advanced sequential thinking capabilities, supporting 19 thinking modes for structured reasoning and complex cognitive tasks.

Provides five rigorous reasoning protocols (debate, red team, audit_argument, threat_model, check_study) that run on the AI you're already using, requiring no extra API keys or costs.

Enables querying AI model lifecycle and deprecation data across OpenAI, Anthropic, Google Gemini, and Amazon Bedrock, including model status, sunset dates, and recommended replacements via MCP tools.

Enables AI agents to discover and query live model information from OpenRouter, HuggingFace, and Ollama Cloud, ensuring recommendations are based on current data.

Enables AI agents and scripts to rug-check any token on Base, Ethereum, Arbitrum, Optimism, Polygon, or Solana, returning a 0-100 risk score with plain-language reasons and a fingerprint for detecting changes on later checks. It also reports market data and supports automatic $0.01 USDC payments via x402.

Enables MCP hosts and AI agents to request pay-per-call token risk assessments for contract addresses across Base, Ethereum, and Solana, returning a verdict, 0–100 risk score, six market-structure signals, and an analyst summary, with payment handled via x402 USDC on Base.

MCP server that pings 130+ free coding LLM models across 17 providers in real-time, ranks them by latency, and helps AI agents pick the fastest available model.

An MCP server for downloading and reading MGX thinking logs, enabling AI agents to fetch and analyze MGX conversation logs programmatically.

Read-only XRPL token and wallet checks with issuer-aware explanations, impersonation checks and spoken summaries. Built for Zach Riehn's XRP Ledger Trading Lab by AI coding agents under Zach's direction. Never signs or sends transactions; findings describe observed ledger evidence, not a guarantee of safety or investment advice. Try it: https://cocoeatsai.com/trading-lab/token-guard/