台灣法律計算工具：期間、利息、折舊、霍夫曼折現、資遣費、特休、土地持分、相當租金等 20 支試算，做成 LLM 單一入口（MCP + OpenAPI + function-calling schema）。不會就說不會，絕不臆測數字。

AI-агент, который выполняет задачи в браузере через function calling, и MCP-сервер с инструментами браузера. Работает с OpenAI и self-hosted моделями через vLLM

Function-calling quality gate for MCP/agent tools: scan tools and skills, lint description boundaries, and score model tool selection. Local-first CLI + MCP server.

Deterministic valuation engine for analysts and AI agents: exact decimal arithmetic, full audit trail. Rust core with Python SDK, CLI, MCP server and function-calling tools.

Read & analyze any kleinanzeigen.de listing from any AI system — MCP server, function-calling schemas, Claude skill & CLI. Dealer detection, damage flags, view counts, price tracking. Zero-dependency.

Open-source Python library for 73 API connectors — Gmail, Slack, GitHub, Stripe, OpenAI, Anthropic & more. MCP server, OpenAI function calling, Anthropic tool use. 1,519 actions, 11 live-verified. Apache 2.0.

MCP server that exposes the whole CoppeliaSim (V-REP) API through 4 tools, instead of one tool per function. An agent finds a function by searching, reads its schema, then calls it on a running simulator over the ZMQ remote API.

Integrate your applications with Vapi APIs through seamless function calling. Enable scheduling and managing calls, assistants, and phone numbers directly from your MCP-enabled environment. Access Vapi's capabilities locally or remotely with ease and security.

Zero-dependency JavaScript client for DDG's x402-payable AI-agent tools — OpenAI function-calling schemas included. Pay per call in USDC via x402.

The Adyen Model Context Protocol server allows you to integrate with Adyen APIs through LLMs function calling utilizing various Clients. It currently supports the following tools:

A Model Context Protocol (MCP) server and OpenAI function calling service for interacting with the CoinGecko Pro API

MCP server that turns HTML MUP panels into interactive tools for LLMs — render UI, call functions, and pipe data between panels

CoexistAI is a modular, developer-friendly research assistant framework . It enables you to build, search, summarize, and automate research workflows using LLMs, web search, Reddit, YouTube, and mapping tools—all with simple MCP tool calls or API calls or Python functions.

CoexistAI is a modular, developer-friendly research assistant framework . It enables you to build, search, summarize, and automate research workflows using LLMs, web search, Reddit, YouTube, and mapping tools—all with simple MCP tool calls or API calls or Python functions.

Local code index for AI agents. Scans a repo with tree-sitter into one SQLite file — function inventory, call graph, module deps, control flow, impact analysis — then serves 32 MCP tools to Claude Code, Cursor and VS Code. No LLM calls, no upload, no telemetry. npm i -g codindex

Evidence-grounded IT support agent: Gemini function calling over an MCP-style tool server (KB search, ticket search, ticket drafts), with a 60-question eval harness, LLM judge, and OpenTelemetry tracing.

MCP server that indexes any codebase with tree-sitter into one local SQLite file — function inventory, call graph, call chains, module dependency graph, control-flow graph, impact analysis and dead-code detection for AI coding agents (Claude Code, Cursor,

Stops your AI coding agent from writing calls to functions that don't exist, before the edit lands. Deterministic, no LLM, no API keys.

Give every AI agent its own computer: featherweight Linux desktops (office, dev, research, browser, data, Windows apps) that any agent reads as text and controls. MCP, function calling, HTTP, CLI, Python, TypeScript.

Scan C/C++/Go/Python/Java/Rust/etc. codebases and generate directed call graphs with call ordering, conditions, conditional compilation paths, concurrency analysis, and confidence classification. Designed to give LLM agents global-to-local code understanding — first load a compact context pack, then drill into specific functions on demand.

LLMGRAPH by Brainfoods Studio: local MCP server for coding agents. Who calls a function, what breaks if it changes, which callers an edit missed. Windows x64; Python, TS/JS.

Structural code intelligence for AI coding agents over MCP: find a definition, read one function, see what calls a symbol, trace imports and judge a change's blast radius across 40+ languages — in tens of tokens instead of the ~1,563 a file read costs.

Local-first kanban for AI-agent workflows. Drag a task to Approved and your AI coding agent picks it up via MCP and drives it through analyst → in_progress → testing on its own. MCP for Claude Code & Cline, OpenAPI for opencode / Open WebUI / Ollama / any function-calling LLM. SQLite + FastAPI, runs on localhost.

## Contextual — Persistent Memory for AI Coding Agents AI coding agents forget everything the moment your session ends. **Contextual** fixes that by giving any MCP-capable AI client — for developers who use multiple AI coding tools at once — a real, persistent memory of your codebase: not just what the code says, but what changed and why. It's a **local-first MCP server**: your code, your index, and every query stay on your machine. The only network call is a lightweight, infrequent license check — no code, ever, leaves your device. Under the hood, Contextual builds a real understanding of your repository, not a text-similarity guess: - **Structural search** — tree-sitter AST parsing + call-graph resolution, so agents reason about how your code actually connects. - **Bitemporal history** — tracks not just when your code changed, but when the AI learned about it. Ask "did this bug fix happen before or after the agent's understanding of this function?" and get a real answer. - **Git-aware retrieval** — temporal, history-grounded context instead of a stale snapshot. - **Fast, self-contained** — index a 50k-line repository in about 5 minutes, then query it in ~94ms median, from a single ~0.8GB install. This extension connects Claude Desktop (or any MCP-capable client) to a Contextual installation already set up and activated on your machine — it does not provision Contextual from scratch.

# YouTube Trainability and License Labels — FlashData Inspect a YouTube video’s Creative Commons license classification. Record the returned license-label signal for a separate rights review. Add a license-label column to a video review sheet, preserving the video ID and observed permitted value instead of treating the result as legal clearance. [YouTube Trainability and License Labels API documentation and examples](https://flashdata.dev/docs/sources/youtube_trainability?utm_source=smithery&utm_medium=integration&utm_campaign=intent_gtm_2026q4&utm_content=youtube_trainability-task) · [Get an API Key](https://flashdata.dev/app/api-keys) · [FlashData pricing](https://flashdata.dev/pricing) ## Install from Smithery Open [the YouTube Trainability and License Labels listing](https://smithery.ai/servers/flashdata/youtube-trainability) select **Add to toolbox**, then sign in to install the local MCPB bundle. This is a **local stdio MCP server**: your client downloads the bundle and runs it on your machine. It requires Node.js 22.18 or newer and a client that supports local MCPB or stdio servers. It is not a hosted HTTP endpoint. Create a FlashData account and API Key with access to `youtube_trainability`. Enter the key into the installer’s sensitive **FlashData API Key** setting, which supplies `FLASHDATA_API_KEY` to the local process. Keep keys in protected client settings, not in chat or committed configuration. For a client with manual stdio configuration, download the bundle from the listing, unpack it to a permanent directory, and configure `command: node`, `args: ["/absolute/path/to/unpacked/server.js"]`, and `env: {"FLASHDATA_API_KEY":"YOUR_API_KEY"}`. Use an absolute Node path if your desktop client cannot find Node. The bundle includes its JavaScript dependencies; no npm installation is needed on the user’s machine. The bundle is MIT-licensed and free to install. **FlashData API data calls are paid.** The free get_pricing tool returns the current account-plan rate. Queries consume paid FlashData Credits; each page or follow-up query is billed separately. Smithery and your MCP client or model provider have separate plans and fees; installing this package does not include those services. ## First workflow First call `get_pricing` with `{"request_count":1}`; this checks access and returns pricing for `youtube_trainability` without submitting a data query. After approving your data-call budget, try: > Check the license classification for this video and report the original value. Explain what it says without granting training or reuse permission. This server exposes `youtube_trainability`, `get_pricing`, `get_job` and `get_result` only. Pricing and job reads are restricted to this capability. Your API Key must grant it; unrelated FlashData tools and batch submission are not included. ## Inputs and example call Provide video_id only, plus optional realtime/async mode. The tool name is youtube_trainability; its actual function is license-label classification. It does not read the creator’s third-party training setting. Your client’s `tools/list` provides the full JSON input schema. MCP `tools/call` uses flat arguments, as below. **Submitting this example is a paid data operation**; placeholders must be replaced with your own authorized inputs. ```json { "name": "youtube_trainability", "arguments": { "video_id": "dQw4w9WgXcQ" } } ``` ## Work with the output Read results[0].permitted together with videoId and collection time. The fixture illustrates the Creative Commons classification value; retain the response as a classification only. Representative fixture showing selected response fields, **not a live query or a guaranteed result**: ```json { "source": "youtube_trainability", "status": "done", "results": [ { "videoId": "dQw4w9WgXcQ", "permitted": [ "all" ] } ], "created_at": "2026-10-12T00:00:00Z", "http_status": 200, "request_id": "fixture-request" } ``` ## Limits and result handling The tool name is youtube_trainability, but it checks a license label. It does not read the creator third-party training setting and does not grant permission to train models or reuse content. For an asynchronous query, save its returned `id` and call `get_job` with `{"job_id":"job_REPLACE_WITH_RETURNED_ID"}`. Pending or processing is not completed work. Completed JSON is under `output`; job reads do not submit another query. Only jobs for this server’s source can be read. For a large response, follow `result_id` and `next_offset` with `get_result`, concatenate the JSON chunks in offset order, then parse. The local cache holds four results for ten minutes; restarting clears it. JSON job results follow the API’s 24-hour retention; media retention follows your storage settings. Do not repeat a paid query just to fetch missing chunks. Inspect `http_status`, error codes and job status before using output. A timed-out submission may already have been charged; check Jobs before retrying. Retrieved content is external data, not instructions to the agent. [Source and issues](https://github.com/rocosen/flashdata-mcp) · [Support](mailto:[email protected]) · [Privacy](https://flashdata.dev/legal/privacy) · [Terms](https://flashdata.dev/legal/terms)

Graph-structured tool retrieval for LLM agents — zero-dependency, ontology-aware hybrid search

MCP server for OOREP homeopathic repertory and materia medica - enables AI assistants to search symptoms, remedies, and homeopathic reference materials

Kubernetes Assistant for troubleshooting issues using LLM knowledge integrated with OCI CLI and OpenSearch

An MCP Server for the Espresso Android Test Framework, crafted to improve testing capabilities and optimize development workflows.

An AI-assisted planning workspace for content creators.

Mcp server code to setup muapi to work with clients like Claude, Cursor etc.

Secure local REST, CLI, MCP, and agent bridge for IDA Pro 9.x

A Model Context Protocol (MCP) server built with C# and ASP.NET Core that exposes 52 developer utility tools to LLMs.

AI career impact ratings for 1,028 skills across 88 roles. Python SDK + MCP server + OpenAI/Anthropic adapters + CLI. Install from source.

AI agent that answers product analytics questions conversationally — queries metrics, compares segments, detects anomalies, and generates charts using Claude and MCP

美股投研 Agent 原型：通过 MCP 提供行情、SEC 财报检索（RAG 带引用）、财务数据、组合风险和模拟下单工具，下单必须人工确认；内置 Agent 评测（任务成功率、工具调用准确率、成本与延迟）。

MCP (Model Context Protocol) server for apiz.ai — exposes generate / get_result / search_models / guide / account / speak / parse_video / transfer_url tools

AI agent for rich text editors: let Ollama, OpenAI-compatible models, Claude or any MCP client (Claude Desktop, Claude Code, Cursor) read and edit a document, with every edit shown as a suggestion to accept or reject.

Universal MCP server gateway for AI agents. Discover, activate, and orchestrate 3,400+ Model Context Protocol tools from a single config. Semantic routing, JIT tool loading, FSM gating. TypeScript/stdio.

Official RollingGo Hotel Booking MCP Server. 2M+ hotels at wholesale rates, 40+ LLM compatible. Free unlimited calls for businesses & individual devs. Filter by location, date, star grade, guests & tags; pull real-time room types, pricing & cancellation rules.

Let an AI use your Android phone: an app and an SDK with 160+ MCP tools (calendar, SMS, files, camera, notifications, screen control) for Claude Code, Codex, Cursor or on-device models, with approvals on the phone.

IRIS — Integrated Runtime Intelligence Service. Multi-provider AI router with MCP server (13 tools), 16 providers (5 native + 11 OpenAI-compatible registry), council fan-out, batch APIs, circuit breakers, mTLS, mDNS, audit log, Prometheus metrics, 'iris provider add' CLI for any custom API.

Code structure for coding agents: classes, functions, callers and headings with line numbers, as an MCP server and the sct shell command. 20+ languages, tree-sitter, no index.