Enables AI agents to reach enterprise apps such as Jira, Microsoft 365, Salesforce, FedEx and Dell TechDirect, or your own REST APIs, through a single self-hosted MCP endpoint. Each agent gets its own key with per-app, per-value grants and a confirmation step for deletes, so calls are scoped and audited without exposing app credentials.

Enables coding agents on different machines to exchange end-to-end encrypted messages through a shared, self-hosted relay, exposing inboxes, room history, and notifications to Claude Code, Codex, or any MCP client. Peers' messages arrive automatically as clearly framed untrusted data, so collaborators can coordinate without granting tool, file, or command access.

Enables Codex to delegate subtasks to concurrent background coding agents that can each run on any model provider supported by Pi, such as DeepSeek, Anthropic, Gemini, OpenRouter, or local models. Exposes MCP tools for listing models, spawning and waiting on agents, sending follow-ups, and aborting, backed by a live local web panel showing status, token usage, and streaming output.

Exposes a single authenticated MCP endpoint that federates to agents running on your own machines, letting you delegate coding jobs to be worked in fresh git worktrees and pushed to a branch, or proxy the stdio MCP servers installed there as tools. Agents dial out through a Cloudflare Tunnel and enforce your local policy, so no inbound ports are opened.

Exposes a federated, typed knowledge graph to AI clients, letting agents traverse nodes and relationships while scope-based access rights are checked on every hop and provenance is tracked for each fact. This keeps shared knowledge consistent across teams while keeping private and team-scoped content out of reach of unauthorized consumers.

Enables agents to discover and invoke browser-registered tools for PDF preview/export, image text overlay/PNG export, URL packing/decoding, and spreadsheet, document, and slide generation.

Enables AI agents to perform real Wolfram Language computation, create and edit native .nb notebooks and figures, export data, and independently verify results through a stdio MCP interface.

Exposes a read_source tool that lets agents read cited tickets, docs, and files through the same path the checker verifies, so quoted business rules match their sources and stay in sync as those sources or their authors change. It also keeps undecided product decisions explicit as open questions rather than letting agents invent answers.

Checks media-plan taste claims against Qloo audience data and returns a verdict per pick with the audience's real preferences. Also exposes what fans of one thing love in another category, so agents can validate picks before spending ad money.

Enables AI agents to generate dimensionally accurate mechanical parts (gears, enclosures, brackets, assemblies) in headless Blender from plain-English specs or JSON parameters, then verify them against manifold, bounding-box and print-production gates and export glTF/STL assets. It also scaffolds interactive Three.js CAD viewer pages with orbit controls, wireframe toggles and exploded-view scrubbing.

Network Sketcher is an AI-ready network design tool with Local MCP, Online, and Offline editions for creating network designs and exporting PowerPoint diagrams and Excel-based configuration data.

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.

Enables agents to plan multi-day travel schedules by solving itinerary graphs with TSP heuristics, fatigue decay, and opening-hour constraints, exposed over MCP STDIO JSON-RPC for agent runtime integration.

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.

Zero-custody DeFi intelligence and control MCP server for AI agents, exposing Trail Heat, wallet and risk intelligence, swap and futures workflows, x402 access, and policy-gated execution. FarmDash never receives customer private keys.

Exposes an assess_repo tool that scans a repository, local folder, or npm/PyPI package and returns supply-chain risk findings with file, line, and code evidence — install-time scripts, dangerous patterns, secrets, AI-agent manipulation, impersonation and published-artifact mismatches — while never executing the target's code. Findings carry severities from info to critical so results can gate installs and CI jobs.

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.

Turns any OpenAPI 3.x spec into an MCP server that exposes one validated tool per API operation, so AI agents can discover and call a REST API over stdio or Streamable HTTP. It is read-only by default, with allow/deny lists, environment-based auth, timeouts and response truncation for safe agent access.

Enables an AI agent to see and control the screen of an already-running macOS virtual machine over VNC, exposing four tools: screenshot, click, type_text, and key_press. This lets the agent drive a disposable VM's UI directly with its own model, without touching the real desktop or running a separate agent loop.

A zero-dependency MCP suite exposing computational tools across metabolic, cardio-renal, chronobiology, neuro-recovery, and biomechanics domains. It enables clients to derive longevity and physiology metrics such as autophagy index, glucose-ketone ratio, glycemic variability, peripheral resistance, nocturnal BP dipping, DLMO clock time, melanopic lux, caffeine clearance, EEG fatigue entropy, and grip vitality age from user-supplied measurements.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.

Exposes four read-only tools over extracted Hacker News "Who is hiring?" job posts — postings_search, posting_get, skills_top, and skills_trend — so an MCP client or agent can search postings, look up individual posts, rank top skills, or chart skill trends over time. Tools return only structured extracted fields and HN links (never raw post text) from a read-only SQLite database, including salary sorting and a hard row limit to keep queries cheap.

Enables an agent to author, render, and visually verify Manim animations from a described concept, returning evidence and structured verdicts so incorrect renders can be fixed before export.