Your Intelligence, Orchestrated. Any Model, Any Agent, Any One.

A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one.

Agentic intelligence framework for Claude Code and Antigravity: slash commands, subagents, skills, hooks, governance, memory, and cost-aware model routing for AI-driven development.

An official Qdrant Model Context Protocol (MCP) server implementation

Enables agents to generate, refine, save, validate, and read sanitized SVG assets from text prompts through a local OpenAI-compatible model, with tools for listing and managing SVG files.

Enables searching sticker collections, viewing sticker images for visual models, and sending stickers—optionally with a short caption—to an authorized QQ session through a running QQ bridge.

Lets Claude and other MCP clients hand coding tasks to the Codex CLI as a sub-agent, running under the user's own Codex configuration so any configured model provider (OpenAI, DeepSeek, local models) does the work. Provides tools to start, poll, cancel and inspect jobs with compact change summaries, long-task handling, token-saving lean mode and clean process teardown.

Enables agents to scan course structures, play videos at real 1x speed, track document task completion, capture quiz screenshots for vision-model reading, select and submit answers, and verify scores through MCP tools.

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 host assistants to discover and read the toolkit’s research methods over a local stdio MCP connection, supporting research planning, literature work, scientific reasoning, writing, figures, and academic document workflows. It does not itself search scholarly databases, generate research results, compile documents, or call models.

Enables coding agents to propose, review, promote, and verify architecture changes as recorded C4 model updates, with hypothetical diffs, evidence-based qualification, and component histories.

Exposes 16 local stdio MCP tools that give Codex-style clients project-isolated persistent memory, local ONNX-based semantic search, and durable task tracking that survives across sessions and concurrent clients, without executing agents, hosting a model, or contacting model providers.

Enables MCP hosts to spawn, supervise, and collect results from parallel panels of pinned-model OpenCode v2 agents for delegated tasks, reviews, and cross-model comparisons. Supports per-member roles, models, permissions, follow-up prompts, cancellation, and persisted result retrieval.

Enables keyword search across source-traceable evidence chunks, listing opportunities linked to supporting evidence, and deterministic ROI calculations with model-proposed inputs and TypeScript-computed dollar figures.

Checks a site for AI crawlers: which of 11 AI crawlers its robots.txt lets in, whether its llms.txt follows llmstxt.org, and what its home page says without scripts. Findings with evidence; no scores, no model calls, no telemetry.

This MCP server enables agents to administer a self-hosted AI control plane for users, organizations, subscriptions, API keys, model providers, and service health, with local-first model routing and cloud failover.

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.

Enables AI hosts to retain evidence, check explicit constraints, plan bounded work, and record outcomes through local-first MCP operations. Optional reconstruction tools also support experimental image-to-3D workflows with local model providers and Blender.

Turns any OpenAPI spec into a single-tool MCP server where the model writes JavaScript against a typed api object in a QuickJS sandbox, so multi-step tasks run in one round trip and only the final result reaches context. Credentials, rate limiting, retries, write gating and audit logging stay on the host, outside the sandbox.

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.

Frontier model solution tracing, then made the prompting skills so Sonnet, GPT, DeepSeek, GLM, Kimi, ETC models work like that

Enables AI agents to run private web searches against a self-hosted SearXNG instance, rerank results with a local model, and fetch or extract pages locally into markdown, including PDFs, with optional caching, crawling, and cited local-LLM answers.

Exposes a single ask_warriorgpt tool that lets Claude Code delegate self-contained code generation prompts to Wayne State's hosted model API, passing files to and from disk so the content never counts against the Claude plan. It validates multi-file edits before writing and logs per-session token usage, keeping design and debugging work with Claude.

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.