Enables installing skills to GitHub Copilot, allowing AI agents to search, discover, and install marketplace skills directly within Copilot.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@skillsmp-mcp-server search for Python skills sorted by stars"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that enables AI agents to search, discover, and install skills from the SkillsMP marketplace.
Tool | Description |
| Search skills by keywords with pagination and sorting |
| AI-powered semantic search using natural language |
| Read skill content (SKILL.md) from GitHub |
| List available skills in a repository |
| Install skills to AI coding agents |
Related MCP server: skilldb-mcp
Node.js 18+
SkillsMP API key (Get one here)
claude mcp add skillsmp -- npx -y skillsmp-mcp-server --env SKILLSMP_API_KEY=your_api_keyAdd to your Cursor MCP configuration (~/.cursor/mcp.json):
{
"mcpServers": {
"skillsmp": {
"command": "npx",
"args": ["-y", "skillsmp-mcp-server"],
"env": {
"SKILLSMP_API_KEY": "your_api_key"
}
}
}
}Add to your Claude Desktop configuration:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"skillsmp": {
"command": "npx",
"args": ["-y", "skillsmp-mcp-server"],
"env": {
"SKILLSMP_API_KEY": "your_api_key"
}
}
}
}Add to your Opencode configuration:
{
"mcp": {
"skillsmp": {
"type": "local",
"command": ["npx", "-y", "skillsmp-mcp-server"],
"env": {
"SKILLSMP_API_KEY": "your_api_key"
},
"enabled": true
}
}
}Add to your Antigravity MCP configuration:
{
"mcpServers": {
"skillsmp": {
"command": "npx",
"args": ["-y", "skillsmp-mcp-server"],
"env": {
"SKILLSMP_API_KEY": "your_api_key"
}
}
}
}Add to your Roo Code MCP settings:
{
"mcpServers": {
"skillsmp": {
"command": "npx",
"args": ["-y", "skillsmp-mcp-server"],
"env": {
"SKILLSMP_API_KEY": "your_api_key"
}
}
}
}Add to your Copilot MCP configuration:
{
"mcpServers": {
"skillsmp": {
"command": "npx",
"args": ["-y", "skillsmp-mcp-server"],
"env": {
"SKILLSMP_API_KEY": "your_api_key"
}
}
}
}Variable | Required | Description |
| Yes | Your SkillsMP API key |
| No | Transport type: |
| No | HTTP port when using http transport (default: 3000) |
Once configured, the MCP server tools become available to your AI assistant.
Search for Python skills sorted by starsFind skills that help with building REST APIs with authenticationWhat skills are available in anthropics/claude-code?Install the frontend-design skill from anthropics/claude-code to Claude CodeSearch skills by keywords.
Parameter | Type | Required | Default | Description |
| string | Yes | - | Search keywords |
| number | No | 1 | Page number |
| number | No | 20 | Results per page (max: 100) |
| string | No | stars | Sort by |
AI-powered semantic search using natural language.
Parameter | Type | Required | Description |
| string | Yes | Natural language query |
Read skill content from GitHub repository.
Parameter | Type | Required | Default | Description |
| string | Yes | - | GitHub username/org |
| string | Yes | - | Repository name |
| string | No | - | Path to skill folder |
| string | No | main | Git branch |
List all skills in a repository.
Parameter | Type | Required | Description |
| string | Yes | GitHub |
Install skills to AI coding agents.
Parameter | Type | Required | Default | Description |
| string | Yes | - | GitHub |
| string | Yes | - | Skill names (comma-separated) |
| string | Yes | - | Target agents (comma-separated) |
| boolean | No | false | Install user-level instead of project-level |
Supported Agents: claude-code, cursor, codex, opencode, antigravity, github-copilot, roo
For remote deployments or multi-client scenarios:
SKILLSMP_API_KEY="your_api_key" TRANSPORT=http PORT=3000 npx skillsmp-mcp-servergit clone https://github.com/anilcancakir/skillsmp-mcp-server.git
cd skillsmp-mcp-server
npm install
npm run build
npm run devnpm run test:runMIT License - see LICENSE for details.
Built with the Model Context Protocol TypeScript SDK.
Semantic search for skills using natural language queries. Powered by AI to understand intent.
Use this when keywords aren't enough - describe what you want to accomplish.
Parameters:
query: Natural language query (e.g., "How to build REST APIs with authentication")
Examples: "tools for web scraping", "help with React testing", "automate deployments"
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language query for AI semantic search (e.g., 'How to create a web scraper', 'tools for SEO optimization') | |
| response_format | No | Output format: 'markdown' for human-readable or 'json' for machine-readable | markdown |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, etc. Description adds the semantic search aspect but no additional behavioral details like pagination or result format. Adequate but not enhanced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise: short paragraph, parameter list, and examples. No wasted words, well-organized for easy scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool without output schema, description provides input guidelines, examples, and response_format parameter hint. Could mention return type (list of skills) but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions already cover both parameters (100% coverage). Description adds example queries which provide context but does not add significant new semantics beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs semantic search for skills using natural language queries. It distinguishes from sibling tools like skillsmp_search by emphasizing intent understanding and contrast with keyword search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this when keywords aren't enough' providing clear when-to-use guidance. Includes examples but does not explicitly name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read skill file content (SKILL.md) from a GitHub repository. Use to preview a skill before installing.
Parameters:
owner: GitHub username/org (e.g., "anthropics")
repo: Repository name (e.g., "claude-code")
path: Path to skill folder (e.g., "plugins/frontend-design/skills/frontend-design")
branch: Git branch (default: "main")
If path has no skill.md, returns directory listing.
| Name | Required | Description | Default |
|---|---|---|---|
| owner | Yes | GitHub repository owner (e.g., 'davila7') | |
| repo | Yes | GitHub repository name (e.g., 'claude-code-templates') | |
| path | No | Path to skill within repo (e.g., 'cli-tool/components/skills/development/senior-prompt-engineer'). If not provided, lists available skills. | |
| branch | No | Git branch (default: 'main') | main |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and idempotentHint=true, indicating safe reads. The description adds valuable behavioral details beyond annotations, such as 'If path has no skill.md, returns directory listing,' which helps the agent understand fallback behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at 6 sentences, front-loaded with the main purpose, then a parameter list with examples, and finally an edge-case behavior. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only preview tool with 4 parameters and no output schema, the description covers parameters, fallback behavior, and usage context. It could mention that the content is raw markdown, but that is implied by 'reads skill file content.' Overall, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds concrete examples for each parameter (e.g., 'owner: GitHub username/org (e.g., "anthropics")' and path examples), which adds meaning beyond the schema's basic descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it reads skill file content (SKILL.md) from a GitHub repository for previewing before installation. The verb 'read' and resource 'skill file content' are specific, and the context 'from a GitHub repository' is unambiguous. It distinguishes from sibling tools like install and list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use to preview a skill before installing,' which provides clear when-to-use guidance. Sibling tools like install and list are implied alternatives, but no explicit when-not-to-use or exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Install skills from GitHub to AI coding agents.
Parameters:
source: GitHub "owner/repo" (e.g., "anthropics/claude-code") [REQUIRED]
skills: Skill names, comma-separated (e.g., "frontend-design,backend-dev") [REQUIRED]
agents: Target agents, comma-separated [REQUIRED] Valid: claude-code, cursor, codex, opencode, antigravity, github-copilot, roo
global: Install user-level instead of project-level (default: false)
Example: source="anthropics/claude-code", skills="frontend-design", agents="claude-code"
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | GitHub shorthand 'owner/repo' (e.g., 'davila7/claude-code-templates'), full GitHub URL, or local path | |
| skills | Yes | Skill names to install (comma-separated: 'skill1,skill2' or array). REQUIRED. | |
| agents | Yes | Target agents (comma-separated: 'claude-code,cursor'). Valid: opencode, claude-code, codex, cursor, antigravity, github-copilot, roo. REQUIRED. | |
| global | No | Install globally (user-level) instead of project-level (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false, so the description correctly implies a write operation. It adds details about installation scope (project vs global) but does not mention side effects like overwriting existing skills or required permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a front-loaded purpose, followed by parameter details and an example. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters and no output schema, the description provides adequate purpose, parameter details, and an example. It lacks information on return values or error handling, but is otherwise complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description rephrases parameters and adds an example but does not significantly extend meaning beyond the schema descriptions (e.g., valid agents are listed in schema too).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Install skills from GitHub to AI coding agents,' which is a specific verb-resource pair. It distinguishes from sibling tools like skillsmp_ai_search or skillsmp_get_skill_content by focusing on installation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains parameters and provides an example but does not explicitly state when to use this tool versus alternatives or when not to use it. Usage is implied but not formally guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available skills in a GitHub repository without installing.
Parameters:
source: GitHub shorthand "owner/repo" (e.g., "anthropics/claude-code")
Returns skill names and descriptions found in the repository.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | GitHub shorthand 'owner/repo' (e.g., 'davila7/claude-code-templates') or full GitHub URL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, non-destructive, idempotent, and open-world. Description adds that it returns skill names and descriptions, providing useful behavioral detail beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Short and to the point, but the parameter list duplicates schema info, slightly reducing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with comprehensive annotations, the description fully covers purpose, parameter, and return content. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for the source parameter. The description provides an example (anthropics/claude-code) but doesn't add significant new meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action (list), resource (skills in a repository), and a key distinction (without installing). Differentiates from sibling install_skill.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly suggests use when exploring repository skills before installation, but lacks explicit when-to-use versus alternatives like search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search SkillsMP marketplace for AI coding skills by keywords.
Parameters:
query: Search terms (e.g., "fastapi", "react testing")
page: Page number (default: 1)
limit: Results per page (default: 20, max: 100)
sort_by: "stars" (default) or "recent"
Returns skills with name, description, author, stars, and GitHub URL.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query for skills (e.g., 'SEO', 'web scraper', 'data analysis') | |
| page | No | Page number for pagination | |
| limit | No | Items per page (default: 20, max: 100) | |
| sort_by | No | Sort results by: 'stars' (default) or 'recent' | stars |
| response_format | No | Output format: 'markdown' for human-readable or 'json' for machine-readable | markdown |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that results include name, description, author, stars, and GitHub URL, and explains pagination defaults and sorting. This extends transparency without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, starting with the purpose then listing parameters in a structured bullet-like format. It uses clear labels and examples without extraneous text. Could slightly benefit from better paragraph flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters and no output schema, the description covers basic functionality (return fields, pagination, sorting) but lacks details on error handling, empty results, or rate limits. It is adequate for a simple search tool but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description repeats schema descriptions for query, page, limit, sort_by and adds examples (e.g., 'fastapi', 'react testing') and clarifies defaults. However, no new semantic meaning beyond schema is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool searches 'SkillsMP marketplace for AI coding skills by keywords', providing a specific verb (search) and resource (marketplace for coding skills). It clearly distinguishes from siblings like skillsmp_install_skill or skillsmp_list_repo_skills by focusing on search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus sibling tools like skillsmp_ai_search or skillsmp_list_repo_skills. It lacks explicit advice on contexts or alternatives, leaving selection to the agent without criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: semantic search, content reading, installation, repo listing, and keyword search. No overlap.
All tools use the 'skillsmp_' prefix followed by a verb_noun pattern (ai_search, get_skill_content, install_skill, list_repo_skills, search), creating a predictable and clear naming convention.
Five tools is an ideal size for a skills marketplace MCP server, covering the core workflow of discovery, preview, and installation without being excessive.
The tool set covers the essential lifecycle (search, preview, install) for skills. Missing uninstall or update tools, but these are secondary to the primary use case.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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