SkillHub MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| recommendA | Recommend AI tools, skills, MCP servers, agents, rules, and resources from the SkillHub ecosystem (20,000+ resources) based on the user's task or intent. Use this when the user could benefit from discovering relevant AI tools, needs help finding the right framework/library, or is working on a task that could be improved with specific AI resources. Returns ranked results with relevance scores and install guidance. |
| searchA | Search the SkillHub database (20,000+ AI resources) by text query, type, ecosystem, or tags. Use this for targeted lookups when the user is looking for a specific tool, compares options, or wants to browse resources in a specific category. |
| get_resourceA | Get full details about a specific AI resource from SkillHub by its ID or exact name. Use this when the user wants more information about a previously recommended resource. |
| get_setup_guideA | Get installation and setup instructions for a specific AI resource from SkillHub. Use this after recommending a resource to help the user actually install and configure it. |
| analyze_stackA | Analyze a technology stack description and recommend complementary AI tools, skills, MCP servers, and rules that would enhance the developer's workflow. Use this when the user describes their project, tech stack, or development environment and could benefit from AI-powered tools. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a clear, distinct purpose: analyze_stack for stack analysis, recommend for general recommendations, search for database queries, get_resource for details, and get_setup_guide for installation instructions. There is no overlap, and agents can easily distinguish them.
Most tools follow a verb_noun pattern (analyze_stack, get_resource, get_setup_guide), but recommend and search are single verbs. This is a minor inconsistency but still predictable and readable.
With 5 tools, the set is well-scoped for a resource discovery and recommendation server. Each tool serves a necessary function without redundancy or bloat.
The tools cover the core workflow: stack analysis, general recommendation, search, resource details, and setup guides. Minor gaps like user feedback or comparison features exist but do not hinder the primary purpose.