list_skills
View all available skills with their descriptions, sources, and document counts to explore capabilities or troubleshoot skill availability.
Instructions
Returns a complete inventory of all loaded skills with their names, descriptions, sources, and document counts. Use this for exploration or debugging to see what skills are available. NOTE: For finding relevant skills for a specific task, use the 'search_skills' tool instead - it performs semantic search to find the most appropriate skills for your needs.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- Primary handler function _handle_list_skills for the MCP stdio server class SkillsMCPServer. Lists all loaded skills with name, description, source (formatted), and document count. Handles loading state.
async def _handle_list_skills(self, arguments: dict[str, Any]) -> list[TextContent]: """Handle list_skills tool calls. Parameters ---------- arguments : dict[str, Any] Tool arguments (empty for list_skills). Returns ------- list[TextContent] Complete list of all skills with metadata. """ response_parts = [] # Add loading status if skills are still being loaded status_msg = self.loading_state.get_status_message() if status_msg: response_parts.append(status_msg) if not self.search_engine.skills: if not self.loading_state.is_complete: return [ TextContent( type="text", text=status_msg or "" + "No skills loaded yet. Please wait for skills to load.", ) ] return [TextContent(type="text", text="No skills currently loaded.")] response_parts.extend( [ f"Total skills loaded: {len(self.search_engine.skills)}\n", "=" * 80, "\n", ] ) for i, skill in enumerate(self.search_engine.skills, 1): # Format source as owner/repo for GitHub URLs import re source = skill.source if "github.com" in source: # Extract owner/repo from GitHub URL match = re.search(r"github\.com/([^/]+/[^/]+)", source) if match: source = match.group(1) doc_count = len(skill.documents) response_parts.append(f"{i}. {skill.name}") response_parts.append(f" Description: {skill.description}") response_parts.append(f" Source: {source}") response_parts.append(f" Documents: {doc_count} file(s)") response_parts.append("") return [TextContent(type="text", text="\n".join(response_parts))] - Standalone handler function handle_list_skills used by the HTTP server. Identical logic to _handle_list_skills but without self references.
async def handle_list_skills( arguments: dict[str, Any], search_engine, loading_state ) -> list[TextContent]: """Handle list_skills tool calls (standalone version for HTTP server).""" import re response_parts = [] # Add loading status if skills are still being loaded status_msg = loading_state.get_status_message() if loading_state else None if status_msg: response_parts.append(status_msg) if not search_engine.skills: if loading_state and not loading_state.is_complete: return [ TextContent( type="text", text=(status_msg or "") + "No skills loaded yet. Please wait for skills to load.", ) ] return [TextContent(type="text", text="No skills currently loaded.")] response_parts.extend( [ f"Total skills loaded: {len(search_engine.skills)}\n", "=" * 80, "\n", ] ) for i, skill in enumerate(search_engine.skills, 1): # Format source as owner/repo for GitHub URLs source = skill.source if "github.com" in source: # Extract owner/repo from GitHub URL match = re.search(r"github\.com/([^/]+/[^/]+)", source) if match: source = match.group(1) doc_count = len(skill.documents) response_parts.append(f"{i}. {skill.name}") response_parts.append(f" Description: {skill.description}") response_parts.append(f" Source: {source}") response_parts.append(f" Documents: {doc_count} file(s)") response_parts.append("") return [TextContent(type="text", text="\n".join(response_parts))] - FastMCP tool registration for list_skills in the HTTP server, including schema (empty input), title, description, and wrapper that delegates to handle_list_skills.
@mcp.tool( name="list_skills", title="List available skills", description=( "Returns the full inventory of loaded skills (names, descriptions, sources, document counts) " "for exploration or debugging. For task-driven work, prefer calling 'find_helpful_skills' first " "to locate the most relevant option before reading documents." ) ) async def list_skills() -> list[TextContent]: """List all loaded skills.""" return await handle_list_skills({}, search_engine, loading_state_global) - Stdio MCP server tool registration in list_tools() and dispatch in call_tool() for list_skills, including schema and handler invocation.
@self.server.list_tools() async def list_tools() -> list[Tool]: """List available tools.""" return [ Tool( name="find_helpful_skills", title="Find the most helpful skill for any task", description=( "Always call this tool FIRST whenever the question requires any domain-specific knowledge " "beyond common sense or simple recall. Use it at task start, regardless of the task and whether " "you are sure about the task, It performs semantic search over a curated library of proven skills " "and returns ranked candidates with step-by-step guidance and best practices. Do this before any " "searches, coding, or any other actions as this will inform you about the best approach to take." ), inputSchema={ "type": "object", "properties": { "task_description": { "type": "string", "description": ( "Description of the task you want to accomplish. Be specific about your goal, " "context, or problem domain for better results (e.g., 'debug Python API errors', " "'process genomic data', 'build React dashboard')" ), }, "top_k": { "type": "integer", "description": f"Number of skills to return (default: {self.default_top_k}). Higher values provide more options but may include less relevant results.", "default": self.default_top_k, "minimum": 1, "maximum": 20, }, "list_documents": { "type": "boolean", "description": "Include a list of available documents (scripts, references, assets) for each skill (default: True)", "default": True, }, }, "required": ["task_description"], }, ), Tool( name="read_skill_document", title="Open skill documents and assets", description=( "Use after finding a relevant skill to retrieve specific documents (scripts, references, assets). " "Supports pattern matching (e.g., 'scripts/*.py') to fetch multiple files. Returns text content or URLs " "and never executes code. Prefer pulling only the files you need to complete the current step." ), inputSchema={ "type": "object", "properties": { "skill_name": { "type": "string", "description": "Name of the skill (as returned by find_helpful_skills)", }, "document_path": { "type": "string", "description": ( "Path or pattern to match documents. Examples: 'scripts/example.py', " "'scripts/*.py', 'references/*', 'assets/diagram.png'. " "If not provided, returns a list of all available documents." ), }, "include_base64": { "type": "boolean", "description": ( "For images: if True, return base64-encoded content; if False, return only URL. " "Default: False (URL only for efficiency)" ), "default": False, }, }, "required": ["skill_name"], }, ), Tool( name="list_skills", title="List available skills", description=( "Returns the full inventory of loaded skills (names, descriptions, sources, document counts) " "for exploration or debugging. For task-driven work, prefer calling 'find_helpful_skills' first " "to locate the most relevant option before reading documents." ), inputSchema={ "type": "object", "properties": {}, "required": [], }, ), ] @self.server.call_tool() async def call_tool(name: str, arguments: dict[str, Any]) -> list[TextContent]: """Handle tool calls.""" if name == "find_helpful_skills": return await self._handle_search_skills(arguments) elif name == "read_skill_document": return await self._handle_read_skill_document(arguments) elif name == "list_skills": return await self._handle_list_skills(arguments) else: raise ValueError(f"Unknown tool: {name}") - Tool schema definition for list_skills with empty input schema (no parameters required).
Tool( name="list_skills", title="List available skills", description=( "Returns the full inventory of loaded skills (names, descriptions, sources, document counts) " "for exploration or debugging. For task-driven work, prefer calling 'find_helpful_skills' first " "to locate the most relevant option before reading documents." ), inputSchema={ "type": "object", "properties": {}, "required": [], }, ),