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bxzymy

MCP Recommender

by bxzymy

recommend_mcp

Find relevant MCP servers for development tasks by describing your functional needs, with options to filter by category and programming language.

Instructions

Recommend MCP servers based on your development needs.

Args: query: Description of the functionality you need (e.g., "database operations", "web scraping", "file management") limit: Maximum number of recommendations to return (default: 5) category: Filter by specific category (optional) language: Filter by programming language (optional)

Returns: Formatted markdown string with MCP recommendations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
limitNo
categoryNo
languageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function for the 'recommend_mcp' tool. It is registered via the @mcp.tool() decorator. This function processes the input parameters (query, limit, category, language), validates them, searches for relevant MCP servers using the recommender instance, formats the results as Markdown, and handles errors.
    @mcp.tool()
    def recommend_mcp(
        query: str,
        limit: int = 5,
        category: str = None,
        language: str = None
    ) -> str:
        """
        Recommend MCP servers based on your development needs.
        
        Args:
            query: Description of the functionality you need (e.g., "database operations", "web scraping", "file management")
            limit: Maximum number of recommendations to return (default: 5)
            category: Filter by specific category (optional)
            language: Filter by programming language (optional)
        
        Returns:
            Formatted markdown string with MCP recommendations
        """
        try:
            # Validate inputs
            if not query or not query.strip():
                return "### Error\nPlease provide a query describing what functionality you need."
            
            if limit < 1 or limit > 20:
                limit = 5
            
            # Search for recommendations
            scored_mcps = recommender.search_mcps(
                query=query.strip(),
                limit=limit,
                category=category,
                language=language
            )
            
            # Format and return results
            return recommender.format_recommendations(scored_mcps, query)
            
        except Exception as e:
            return f"### Error\nAn error occurred while searching for recommendations: {str(e)}"
  • The @mcp.tool() decorator registers the recommend_mcp function as an MCP tool.
    @mcp.tool()
  • The function signature and docstring define the input schema (query: str, limit: int=5, category: str=None, language: str=None) and output (str with Markdown recommendations).
    def recommend_mcp(
        query: str,
        limit: int = 5,
        category: str = None,
        language: str = None
    ) -> str:
        """
        Recommend MCP servers based on your development needs.
        
        Args:
            query: Description of the functionality you need (e.g., "database operations", "web scraping", "file management")
            limit: Maximum number of recommendations to return (default: 5)
            category: Filter by specific category (optional)
            language: Filter by programming language (optional)
        
        Returns:
            Formatted markdown string with MCP recommendations
        """
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns 'formatted markdown string with MCP recommendations' but doesn't describe the recommendation algorithm, whether results are ranked, what data sources are used, or any limitations (rate limits, freshness of data, etc.). For a recommendation tool with zero annotation coverage, this is insufficient behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured and concise. It begins with a clear purpose statement, then provides organized parameter documentation with helpful examples, and concludes with return value information. Every sentence earns its place, and the information is front-loaded with the most important details first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, 1 required), no annotations, but with an output schema, the description provides good coverage. The parameter semantics are well-explained, and the existence of an output schema means the description doesn't need to detail return values. However, it lacks guidance on when to use this versus sibling tools, which is a notable gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides excellent parameter semantics despite 0% schema description coverage. It clearly explains each parameter's purpose with examples: 'query: Description of the functionality you need (e.g., "database operations", "web scraping", "file management")', 'limit: Maximum number of recommendations to return (default: 5)', and clarifies optionality for category and language. This fully compensates for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Recommend MCP servers based on your development needs.' It specifies the verb ('recommend'), resource ('MCP servers'), and context ('development needs'). However, it doesn't explicitly differentiate from sibling tools like 'get_functional_keywords' or 'list_categories' - it's clear what this tool does, but not how it's distinct from those alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 the sibling tools 'get_functional_keywords' and 'list_categories'. While it mentions the tool's purpose, it doesn't indicate scenarios where this recommendation tool is preferable to the keyword or category listing tools, nor does it mention any prerequisites or constraints for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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