AI Workbench MCP
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- AlicenseAqualityAmaintenanceExposes an agent skill, reference guides, and Blender Python script templates over stdio so AI agents can follow a structured modeling workflow and retrieve validation/export resources. It does not itself control a live Blender scene and must be paired with a separate Blender-control MCP server for live operations.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables users to manage, search, render, version, and regression-test local Markdown templates with typed variables, and to expose them as MCP tools and native prompts without requiring a model API, account, or cloud service.MIT

@event4u/agent-configofficial
AlicenseAqualityNot gradedmaintenanceUniversal AI Agent OS — governed skills, rules, and commands for AI coding assistants (Claude Code, Augment, Cursor, Copilot, Windsurf). Read-only MCP bridge serves prompts and resources from a release-pinned content bundle.261,196 npm18MIT- FlicenseBqualityCmaintenanceConnects an LLM to specialized MCP servers via stdio, enabling authenticated GitHub repository, issue, file, and code operations alongside TF-IDF retrieval over employee PDF records and QA test-case generation and result validation. It exposes tools, a read-only resource, and reusable prompt templates so a client can discover and orchestrate multiple capabilities in one workflow.3-
- AlicenseNot gradedqualityBmaintenanceServes skill-defined JSON-in/JSON-out scripts as callable tools over MCP stdio, driven by a portable skill-runtime.json manifest that supplies each tool's title, description, and input schema. Exposes commands to validate, list, and run skills, so hosts can discover and invoke agent skills without custom per-skill glue code.13 npmMIT
- AlicenseNot gradedqualityCmaintenanceEnables AI tools to uniformly discover, inspect, and call tools, prompts, and resources from multiple upstream MCP servers through a small set of fixed MCP tools, over stdio or HTTP.2MIT
TDQS
Scored across 3 tools
Each tool targets a distinct action and resource: list_prompts enumerates the catalog, render_prompt renders a specific template, and get_assistant retrieves a blueprint. The list vs get distinction is clear, with no overlapping purposes.
All names follow a consistent verb_noun snake_case pattern: list_prompts, render_prompt, get_assistant. Pluralization varies logically (list many vs render/get one), but the convention is uniform.
Three tools are well-suited to a read-only local catalog, covering the essential operations of enumerating, rendering, and retrieving. The count is minimal but each tool earns its place, with no bloat or missing core function.
The surface covers listing, rendering, and retrieving, but lacks a direct way to get a raw prompt template without rendering it (e.g., get_prompt) or a generic assistant blueprint without host selection. These are minor gaps for a small catalog that agents can likely work around.