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Advanced GitLab MCP server

An automated QA assistant for AI-written code. Gives your coding agent a verify_feature tool over MCP.

Framework-aware code intelligence MCP server — 91 framework integrations, 81 languages, 72.7% fewer input tokens to review a pull request, comprehension at parity

Enables coding agents to audit projects locally for token waste, calculate model arbitrage and pricing alternatives, and make request bodies cache-friendly, with payload optimization sending the supplied request body to the TokenZero service.

Enables AI agents to request Jupiter swap quotes, build unsigned Solana swap transactions for user signing, and access paid token-risk and wallet-portfolio data via x402.

Enables AI agents to answer data-center siting questions by querying a live facility registry, per-metro market intelligence, real-time grid fuel mix and demand, and leasable capacity listings, including a one-call planner that combines these into ranked, cited answers. Results are fetched live per request, keyless, with optional API keys for fuller responses.

Search and buy products from sellers and verified warehouses, sell surplus stock, post requests.

Gives AI agents one MCP interface to inspect CI health and flaky tests, pull request flow, delivery/DORA metrics, dependency risks, community health, insights, and org portal data across GitHub repositories. It exposes 11 core tools by default and all 35 with --tools all.

Enables an AI agent to ask your phone for its current location on demand with consent, via a self-hosted relay and OAuth instead of API keys. The relay requests a fresh GPS fix and returns the answer without keeping location history.

Enables AI agents to drive policy evaluation over MCP by starting executions, answering policy questions from case evidence, and reading deterministic outcomes with reasoning traces, while the runtime controls which questions are asked and how answers combine.

Enables Claude Code users to inventory the MCP servers, skills, commands, agents and plugins installed on their machine, estimate how many tokens each one adds to every request, and switch them off per project or globally, including across other AI coding tools. It also monitors live sessions to report real token usage and the savings achieved from disabled items.

Enables AI agents to queue, commit, push, and track durable Git transactions through a shared daemon, preserving other agents' unstaged work and allowing recovery by request ID.

Enables Claude Code to delegate tasks to persistent GitHub Copilot CLI sessions via MCP, inspect pending tool requests, and approve each operation once or deny it while continuing multi-turn context. It bridges ACP runtime sessions locally over stdio without a public service or background daemon.

Enables listing models and running non-streaming chat completions against RelayRouter's OpenAI-compatible API, so agents can discover available models and send chat requests through a key-configured MCP endpoint.

Enables an external AI client to discover tender portal APIs, collect and verify tender listings, combine sections, and select relevant lots from a user request through local MCP tools.

Enables agents to reach pump.fun's full API surface — official developer routes, bonding-curve reads, Solana RPC, and unsigned trade building — by writing JavaScript against one merged OpenAPI document, using just two tools (search and execute). Model-authored code runs in a network-less sandbox, so multi-step questions resolve in a single round trip while credentials stay on the host Worker.

Embeds a runtime MCP inspector in a NestJS app so coding agents can inspect the module and provider graph, routes and their guards/interceptors/pipes/filters, logs, request traces, event listeners, and scheduled jobs through an HTTP endpoint.

Enables AI agents to build, validate, and submit ledger-native XRPL transactions against public nodes — escrows, XRP and token payments, DEX/AMM swaps and liquidity, issuer clawback, and x402 pay-per-request — with validated-ledger proof of results. State-changing calls go to the account owner for browser signing, while an owner-funded agent account allows autonomous spending bounded by on-ledger balance and local policy limits.

Keeps large tool outputs out of an agent's context window by spilling them to disk and handing back a short pointer instead. Lets the agent pull only what it needs — exact numbered line ranges read directly from disk, or verbatim relevant lines returned by a small model answering a question about the stored output, plus tools to open, list, and close documents.

Lets any MCP-capable AI agent call a hosted EdgePress site's REST API to browse and manage CMS content or store data, using a personal access token whose role and read-only flag decide what the agent can do. Supports Streamable HTTP at /api/mcp or local stdio via npx, with an OpenAPI spec lookup tool for discovering routes and request bodies.

Enables MCP-capable agent CLIs on macOS to reuse an installed, unmodified OpenAI Browser Runtime to drive a connected Chrome or Edge browser (or a host's in-app browser backend) through js, js_reset, and turn_ended tools. It stays a thin bridge that preserves the Runtime's approval and security boundaries, failing closed when the client cannot handle approval requests.

Enables people and AI agents to create, preview, publish, withdraw, and discover offers and requests across goods, services, trips, and delivery, with semantic matching and secure shared rooms for negotiation.

It enables AI agents to access live web data through page fetching, search, prebuilt scrapers, datasets, and remote browsers, using free direct requests and local Chrome rendering with Bright Data only as a fallback for bot walls. It also adds budget controls, caching, pagination, cancellation, retries, SSRF protection, and both stdio and HTTP transports.