Bridges Hermes Agent to the Hermes Intelligence Platform API, enabling tools to read/write learning loop data (context, feedback, signals, memory, etc.) via stdio.
Connects AI assistants to your Argo campaigns via the Model Context Protocol. Once configured, your AI assistant can read and write campaign lore, look up character details, and interact with Argo data directly from the chat interface.
MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.
MCP server giving Claude full control over ElevenLabs Conversational AI agents, conversations, knowledge base, tools, tests, telephony, and workspace settings.
Exposes multiple LLM providers (AWS Bedrock, OpenAI, Google Gemini, local Ollama) as MCP tools with automatic routing by task type and Prometheus metrics, enabling any MCP-compatible client to generate text, route prompts, and list providers.
MCP server for MarkItUp's AI image-annotation pipeline. Generate polished marketing-visual variations of any screenshot, regenerate, AI outpaint, and remove backgrounds —
powered by Claude analysis + Gemini rendering.
A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports.
Enables text-only agents to process images by accepting image files, base64 data, or URLs, sending them to multimodal models, and returning structured text results via MCP.
Enables product enrichment with sentiment analysis, category mapping, and attribute extraction; provides tools to fetch top products and cluster summaries.
Enables Claude Code to use GLM (Zhipu) as a cheap, full-capability subagent for file editing, code generation, and bash commands, with automatic routing between Opus and GLM based on task complexity.
Enables context-aware memory and multi-model orchestration through tools for storing/retrieving dialog turns, searching memory, and routing to AI providers based on task types.