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ajragusa

perfsonar-mcp

by ajragusa

find_pscheduler_services

Discover pScheduler services to run network performance tests. Filter by city or country to locate testpoints for monitoring throughput, latency, and packet loss.

Instructions

Find pScheduler services for running tests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationCityNoCity filter
locationCountryNoCountry filter

Implementation Reference

  • The core handler implementation for finding pScheduler services, which uses the lookup service client to query for records with type 'service' and service_type 'pscheduler'.
    async def find_pscheduler_services(
        self,
        location_city: Optional[str] = None,
        location_country: Optional[str] = None,
    ) -> List[LookupServiceRecord]:
        """
        Find pScheduler services for running tests
    
        Args:
            location_city: Filter by city
            location_country: Filter by country
    
        Returns:
            List of pScheduler service records
        """
        logger.info(
            f"Finding pScheduler services (city={location_city}, country={location_country})"
        )
        params = LookupQueryParams(
            type="service",
            service_type="pscheduler",
            location_city=location_city,
            location_country=location_country,
        )
        return await self.search_records(params)
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 finds services for running tests but doesn't disclose key traits like whether it's read-only or mutative, authentication needs, rate limits, or what the output format looks like. 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's front-loaded with the core purpose and appropriately sized for the tool's complexity.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'pScheduler services' are, what 'running tests' entails, or what the return values look like (e.g., a list of services, their statuses). For a tool with no structured behavioral data, more context is needed to guide the agent effectively.

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?

Schema description coverage is 100%, so the input schema already documents both parameters ('locationCity' and 'locationCountry') as filters. The description implies filtering by location but doesn't add meaning beyond what the schema provides, such as how filters combine or examples of usage. 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.

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 a specific verb ('Find') and resource ('pScheduler services'), making it understandable. However, it doesn't distinguish this tool from sibling tools like 'lookup_testpoints' or 'query_measurements', which might also involve finding or querying network-related data, so it lacks explicit differentiation.

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 doesn't mention any prerequisites, context for use, or exclusions, leaving the agent to infer usage based on the tool 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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