Governance standard and reference toolset for LLM-maintained knowledge corpora

Multi-agent team operating system for Claude Code. 108 MCP tools, 40+ agent templates, 10 lifecycle hooks, 7 pipeline workflows. Persistent teams, structured meetings, task wall, real-time React dashboard. No LangChain/AutoGen — pure CC native integration.

Enables agents to validate enterprise travel policies, audit hotel rate parity, and check carbon ESG compliance through a zero-dependency MCP server. It exposes policy and expense-auditing tools over STDIO JSON-RPC for use in Claude Desktop, Cursor, or other MCP runtimes.

An MCP server that lets AI agents automate Microsoft Edge, organizing sessions into labelled tab-group workspaces or separate profiles and providing tools for browser control, screenshots, extraction, and secure sign-in through Edge's password manager.

Exposes the full Plexim PLECS Standalone JSON-RPC interface as MCP tools, letting agents auto-start and connect to PLECS to load and edit models, run simulations and analyses, manipulate scopes and webserver data, and perform code generation or FMU export with structured, size-aware results. Includes write guardrails, concurrency handling for long simulations, and batch/raw passthrough escape hatches.

Enables an AI agent or any MCP host to discover and call keyless web search, safe arithmetic, constrained HTTP fetching, and current-time tools over stdio, with model tool-calling powered by Groq's free API or local Ollama.

Enables an AI agent to drive a remote Linux machine over the LAN through a single TCP connection, exposing tools for shell commands, jobs, interactive PTYs, file read/write/search/transfer, process and service management, networking, packages, and power control, with optional multi-host switching. Operates without SSH or any Linux-side installation beyond one stdlib-only Python daemon, using a state-free binary protocol.

Exposes a governed dbt MetricFlow semantic layer to AI agents through three tools — list metrics, list dimensions and query metrics — so questions about federal contract spend are answered from defined metrics rather than raw tables. Guardrails reject undefined metrics, raw columns, SQL in filters and bulk exports, and no tool can execute SQL.

Enables agents to verify AI product claims through Tencent Yuanbao search with cited evidence and authenticity verdicts, find dramas or songs from quoted lines, and check search quota usage via five MCP tools.

Enables learners to browse, read, and be quizzed on saved lessons inside Claude Desktop and Claude Code, and enables five Claude agents to build fact-checked K–College lessons through guarded, pinned MCP tools.

Enables agents to run SweetScape 010 Editor Binary Templates and Scripts headlessly against local files. It provides tools to locate the editor binary, execute templates or scripts, and use controlled exit behavior for compile and runtime failures.

Enables AI agents to query and trade through Interactive Brokers using 38 tools for order placement and management with risk checks, account and position data, market and historical data, and options analytics. Includes diagnostics and configurable safety limits, designed primarily for paper trading.

Enables AI agents to operate a self-hosted double-entry personal finance ledger: answering spending, net-worth and cash-flow forecast questions, searching transactions, and managing schedules, rules and imports through audited, reversible tools. Read and write access is scoped by token, with privacy controls over what financial details reach the agent.

A read-only MCP server that lets AI agents query a user's own Garmin Connect data via Garmin's official Personal API. It exposes activities, daily wellness metrics, sleep and recovery, and body/profile data as tools for natural-language analysis.

Enables agents to perform autonomous multi-angle specular glare healing and planar foil defect metrology by ranking candidate views, fusing glare-free views, and finalizing inspection or escalating unresolved cases through MCP tools.

Enables AI agents to query a Python codebase as a knowledge graph over MCP, exposing symbol search plus callers, callees, and module dependency tools.

Enables agents to interact with a ServiceNow instance through thirteen consolidated tools covering records, schema, CMDB graph traversal, catalog, attachments, code, operations, knowledge, and administration, with a local store for notes, cache, saved queries, history, and revertible writes.

Exposes agent-facing tools that let a personal AI agent submit each proposed action for an allow, require_approval, or deny verdict against OWASP PAA10 policy rules, plus tools to record provenance-tagged memories, log refusals, read status, and trigger a self-stop. Its decisions are advisory unless paired with the project's gating proxy or hook, which enforce them.

Connects MCP hosts and LangChain agents to a local WAHA (WhatsApp HTTP API) instance, exposing tools for session management, chats, messages, contacts, and sending text, images, or files. It also provides an operational skill prompt covering lifecycle, chat ID formats, send guardrails, media rules, and error handling.

Enables AI agents to read WeChat Official Account articles by fetching mp.weixin.qq.com links from the user's own machine and network and converting them into clean Markdown with title, account, author, and publish-time metadata. Provides both single-article and batch-reading tools (up to 20 URLs) with optional image handling, character limits, and JSON output.

Enables LangChain ReAct and deep agents to manage WhatsApp sessions, chats, contacts, and messages—including sending text, images, and files—through MCP tools backed by a local WAHA instance.

Enables MCP-compatible apps to expose a folder of RAPP agent.py files as callable tools, deriving each tool's schema from its metadata and running calls in isolated, timed processes.

Enables agents to estimate latent user intent, maintain belief states, and resolve ambiguous requests using entropy-based thresholding through MCP tools.

Enables agents to store LLM query-response pairs and retrieve them via fuzzy cosine similarity matching, reducing duplicate inference calls; it exposes tools for setting cache entries, semantic lookup, and hit-rate statistics.