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easyhak

YouTube Search & Download MCP Server

by easyhak

validate_provider

Check if the YouTube search provider functions correctly to ensure reliable video search and download operations within the MCP server environment.

Instructions

Validate that the YouTube search provider is working correctly.

Returns: JSON with validation status and provider information

Example: validate_provider()

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The implementation of the validate_provider tool, which checks the status of the search provider and returns a JSON response.
    @mcp.tool()
    async def validate_provider() -> str:
        """
        Validate that the YouTube search provider is working correctly.
    
        Returns:
            JSON with validation status and provider information
    
        Example:
            validate_provider()
        """
        try:
            provider = get_search_provider()
            is_valid = await provider.validate_connection()
    
            return json.dumps(
                {
                    "valid": is_valid,
                    "provider": "yt-dlp",
                    "status": "operational" if is_valid else "error",
                },
                indent=2,
            )
        except Exception as e:
            logger.error(f"Provider validation failed: {e}")
            return json.dumps(
                {"valid": False, "provider": "yt-dlp", "status": "error", "error": str(e)}, indent=2
            )
  • The function that registers the utility tools, including validate_provider, with the MCP server.
    def register_utility_tools(mcp: FastMCP) -> None:
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 validates the provider and returns JSON with status and information, which covers basic output behavior. However, it lacks details on potential side effects (e.g., does it perform network calls, log data, or affect system state?), error handling, or performance characteristics (e.g., latency, rate limits). For a tool with zero annotation coverage, this is insufficient to fully inform the agent.

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

Conciseness4/5

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

The description is concise and front-loaded, with the first sentence clearly stating the purpose. The 'Returns:' and 'Example:' sections are brief and relevant, adding value without redundancy. However, the example 'validate_provider()' is minimal and could be slightly expanded to illustrate typical output, but overall, the structure is efficient with little waste.

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

Completeness3/5

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) and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the core purpose and output format. However, it lacks context on when to use it relative to siblings and misses behavioral details like side effects or error handling, which are important for a validation tool even with simple inputs. This results in a moderate score.

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, and the input schema has 100% description coverage (though empty). The description does not need to compensate for any parameter gaps, as there are none. It appropriately omits parameter details, aligning with the schema. The baseline for 0 parameters is 4, as the description correctly focuses on the tool's function without unnecessary parameter explanations.

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: 'Validate that the YouTube search provider is working correctly.' It specifies the verb 'validate' and the target 'YouTube search provider,' making the intent unambiguous. However, it does not explicitly differentiate this tool from its siblings (e.g., search_videos, get_video_info), which are all related to YouTube operations but serve distinct purposes.

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 no prerequisites, context for usage (e.g., troubleshooting, initialization), or comparisons to sibling tools. The example 'validate_provider()' is purely syntactic and does not offer usage context. This leaves the agent without clear direction on appropriate scenarios for invocation.

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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