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ajragusa

perfsonar-mcp

by ajragusa

get_throughput

Measure network throughput between source and destination hosts using historical performance data from perfSONAR archives.

Instructions

Get throughput measurements between source and destination.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesSource host/IP address
destinationYesDestination host/IP address
timeRangeNoTime range in seconds
summaryWindowNoSummary window in seconds

Implementation Reference

  • The MCP tool handler for 'get_throughput' which calls the perfsonar_client and formats the result as a JSON string.
    async def get_throughput(
        source: str,
        destination: str,
        timeRange: int = 86400,
        summaryWindow: Optional[int] = None,
    ) -> str:
        """Get throughput measurements between source and destination.
    
        Args:
            source: Source host/IP address
            destination: Destination host/IP address
            timeRange: Time range in seconds (default: 86400 = 24 hours)
            summaryWindow: Summary window in seconds for aggregation
    
        Returns:
            JSON string with throughput measurement data
        """
        results = await perfsonar_client.get_throughput(source, destination, timeRange, summaryWindow)
        return json.dumps([r.model_dump(by_alias=True) for r in results], indent=2)
  • The underlying client implementation that fetches the actual throughput measurement data.
    async def get_throughput(
        self,
        source: str,
        destination: str,
        time_range: Optional[int] = None,
        summary_window: Optional[int] = None,
    ) -> List[MeasurementResult]:
        """
        Get throughput measurements between source and destination
    
        Args:
            source: Source host/IP address
            destination: Destination host/IP address
            time_range: Time range in seconds from now
            summary_window: Summary window in seconds
    
        Returns:
            List of measurement results
        """
        logger.info(f"Getting throughput: {source} -> {destination}")
        metadata = await self.query_measurements(
            MeasurementQueryParams(source=source, destination=destination, event_type="throughput")
        )
    
        results = []
        for meta in metadata:
            event_type = next((e for e in meta.event_types if e.event_type == "throughput"), None)
            if not event_type:
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 but only states the basic function. It doesn't cover critical aspects like whether this is a read-only operation, if it requires authentication, rate limits, error handling, or what the output format might be, leaving significant gaps.

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 no wasted words, clearly front-loading the core purpose. It's appropriately sized for the tool's complexity, 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 no annotations and no output schema, the description is incomplete for a tool with 4 parameters and network measurement complexity. It fails to explain behavioral traits, return values, or usage context, leaving the agent under-informed despite the clear schema.

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 schema fully documents all parameters. The description adds no additional meaning beyond implying that parameters define the measurement context, which aligns with the schema but doesn't enhance it, meeting the baseline for high coverage.

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 action ('Get') and resource ('throughput measurements') with scope ('between source and destination'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_latency' or 'get_packet_loss' that also retrieve network metrics, missing full sibling distinction.

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. The description lacks context about prerequisites, timing, or comparisons to siblings like 'get_latency' or 'schedule_throughput_test', leaving the agent with no usage direction.

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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