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ajragusa

perfsonar-mcp

by ajragusa

lookup_testpoints

Find perfSONAR testpoints by filtering with service type, city, or country to locate network measurement endpoints for performance testing.

Instructions

Find perfSONAR testpoints using the lookup service.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
serviceTypeNoService type filter
locationCityNoCity filter
locationCountryNoCountry filter

Implementation Reference

  • The 'lookup_testpoints' tool is defined as an @mcp.tool() in fastmcp_server.py. It uses the 'lookup_client' to fetch testpoints and serializes the result to JSON.
    @mcp.tool()
    async def lookup_testpoints(
        serviceType: Optional[str] = None,
        locationCity: Optional[str] = None,
        locationCountry: Optional[str] = None,
    ) -> str:
        """Find perfSONAR testpoints using the lookup service.
    
        Args:
            serviceType: Service type filter (e.g., 'bwctl', 'owamp')
            locationCity: City filter (e.g., 'Chicago', 'London')
            locationCountry: Country filter (e.g., 'US', 'GB')
    
        Returns:
            JSON string with list of matching testpoints
        """
        results = await lookup_client.find_testpoints(serviceType, locationCity, locationCountry)
        return json.dumps([r.model_dump(by_alias=True) for r in results], indent=2)
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 it 'finds' testpoints, implying a read-only query, but doesn't cover aspects like rate limits, authentication needs, error handling, or what the output looks like (e.g., list format, pagination). This is a significant gap for a tool with no annotation coverage.

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 a single, efficient sentence with zero waste—it directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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

Completeness2/5

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

Given the complexity (a query tool with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return values, error cases, or behavioral traits, leaving the agent with insufficient context to use the tool effectively beyond basic parameter passing.

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

Parameters3/5

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

The schema description coverage is 100%, so the input schema already documents all three parameters (serviceType, locationCity, locationCountry) with basic descriptions. The description adds no additional meaning or examples beyond this, such as explaining what 'serviceType' might include or how filters combine. Baseline 3 is appropriate when the schema does the heavy lifting.

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

Purpose3/5

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

The description states the action ('Find') and resource ('perfSONAR testpoints') with the method ('using the lookup service'), providing a basic purpose. However, it doesn't differentiate from sibling tools like 'find_pscheduler_services' or 'query_measurements', which might involve similar search/find operations, leaving the scope vague.

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., for network performance testing), or exclusions, leaving the agent to infer usage from the name and parameters alone.

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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