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bxzymy

MCP Recommender

by bxzymy

get_functional_keywords

Retrieve functional keyword mappings to improve search accuracy and discover relevant MCP server recommendations.

Instructions

Show available functional keyword mappings for better search results.

Returns: Formatted list of functional keywords

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function decorated with @mcp.tool(), implementing the logic to retrieve and format functional keywords from the recommender instance.
    @mcp.tool()
    def get_functional_keywords() -> str:
        """
        Show available functional keyword mappings for better search results.
        
        Returns:
            Formatted list of functional keywords
        """
        try:
            if not recommender.functional_keywords:
                return "### No functional keywords available\nThe keyword mapping database is not loaded."
            
            result = "### Functional Keywords Mapping:\n\n"
            result += "Use these terms in your queries for better results:\n\n"
            
            for function, keywords in recommender.functional_keywords.items():
                result += f"**{function.title()}:**\n"
                result += f"  - Keywords: {', '.join(keywords[:8])}"  # Show first 8 keywords
                if len(keywords) > 8:
                    result += f" (and {len(keywords) - 8} more)"
                result += "\n\n"
            
            return result
            
        except Exception as e:
            return f"### Error\nAn error occurred while retrieving keywords: {str(e)}"
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Returns: Formatted list of functional keywords,' which gives some output information, but it doesn't disclose critical behavioral traits such as whether it's read-only (implied by 'Show' but not explicit), potential rate limits, authentication needs, or any side effects. For a tool with no annotations, this is a significant gap in transparency.

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 appropriately sized and front-loaded, consisting of two sentences that directly state the purpose and return value without any wasted words. Every sentence earns its place by providing essential information, making it highly efficient and well-structured for quick understanding.

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 low complexity (0 parameters, no annotations, but has an output schema), the description is reasonably complete. It explains what the tool does and what it returns, and since an output schema exists, it doesn't need to detail return values further. However, it could improve by adding more behavioral context, such as usage scenarios or limitations, to better guide the AI agent.

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 tool has 0 parameters with 100% schema description coverage, so the input schema fully documents the lack of parameters. The description doesn't add any parameter-specific information, which is appropriate here. According to the rules, with 0 parameters, the baseline score is 4, as there's no need for the description to compensate for schema gaps.

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 with 'Show available functional keyword mappings for better search results,' which is a specific verb ('Show') and resource ('functional keyword mappings'). It distinguishes from sibling tools like 'list_categories' and 'recommend_mcp' by focusing on keyword mappings rather than categories or recommendations. However, it doesn't explicitly differentiate from siblings in the text, so it's not a perfect 5.

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 alternatives. It mentions 'for better search results,' which implies a context of search optimization, but there's no explicit mention of when to choose this over sibling tools like 'list_categories' or 'recommend_mcp,' nor any prerequisites or exclusions. This lack of specific usage instructions limits its effectiveness for an AI agent.

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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