"A server to manage API rate limiting based on requests per minute" matching MCP servers:

Converts any REST API into MCP-compatible tools instantly by providing an OpenAPI/Swagger spec, enabling seamless integration with AI agents.

Acts as a bridge between standard REST/OpenAPI web APIs and the Model Context Protocol, dynamically converting REST endpoints into MCP tools for LLM clients like Cursor, Claude Desktop, and n8n.

Automatically converts OpenAPI/Swagger documents into MCP servers, enabling AI agents to call third-party REST APIs via the MCP protocol.

Enables high-throughput API rate limiting with deterministic token bucket throttling and burst quota management for AI agents.

Fleet-wide shared rate limiter for A2A + multi-MCP deployments, providing a shared counter with sliding window, concurrency grants, and signed enforcement attestations.

Enables running a stateless Model Context Protocol server over Streamable HTTP on Cloudflare Workers or any fetch-capable runtime, with signed sessions, origin checks, and JSON-RPC request handling. It lets developers define tools with JSON Schema validation and per-request context.

Enables adaptive token bucket rate limiting and backoff scheduling to prevent HTTP 429 errors for autonomous agents and API clients. Provides a deterministic zero-dependency MCP/CLI engine for acquiring token permits and returning structured telemetry.

Enables zero-dependency INT8 symmetric quantization of LLM weights and activations on a per-tensor or per-channel basis, reporting SNR and MSE reconstruction telemetry along with TTFT/TPOT latency and jitter metrics. Also exposes edge inference primitives such as PagedAttention block allocation, radix prefix caching, and speculative decoding verification through the Model Context Protocol.

Enables Claude Code, Cursor, and Codex users to orchestrate plan/build/review workflows across host or CLI models with a spec gate, tool vetting, hard per-run and per-day cost/token limits, safe defaults, and clear logs.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

Enables autonomous agents to apply adaptive token bucket concurrency limits and backoff scheduling to avoid HTTP 429 errors. It provides deterministic rate-limiting permits and structured telemetry through MCP-compatible clients.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.

Enables AI agents and multi-agent frameworks to throttle outbound LLM API calls with multi-provider token-bucket and sliding-window limiting, plus predictive token-consumption budgeting to avoid TPM/RPM throttling. It runs deterministically over MCP with zero dependencies, returning rate-limit decisions and telemetry for any client such as Claude Desktop or Cursor.

Enables AI agents to validate AI infrastructure claims, check endpoint availability, solve CAPTCHA challenges, convert web pages to Markdown, search the web, extract structured JSON, and fetch bot-protected HTML through native tool calls.

An MCP server that turns a one-sentence idea into working, tested code using a pipeline of six AI agents, with optional deployment.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Enables MCP clients such as Claude Desktop to hand tasks to a running DeepSeek Harness window, where each task becomes a real session visible in the sidebar with a reusable session id. If the in-process plugin is unavailable, it falls back to running the task headless so the tool still returns a result.

Turns any OpenAPI/Swagger specification into a functional MCP server, dynamically exposing API endpoints as LLM tools or generating standalone Python code.

Enables AI agents and MCP-compatible clients to enforce multi-provider token-bucket and sliding-window rate limits, preventing TPM/RPM throttling across LLM APIs with predictive token consumption budgeting.