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AiBadgr

Badgr — GPU Compute for AI Agents Copy

Official
by AiBadgr

check_result

Check a Badgr deployment's status and fetch its latest log lines using the deployment_id to verify workload progress and detect issues.

Instructions

Check the state of one Badgr deployment and fetch its most recent log lines. Use after run_workload or deploy_model, with the returned deployment_id. Call badgr_down when the deployment is finished so billing stops.

Input Schema

NameRequiredDescriptionDefault
log_linesNoNumber of most recent log lines to return (default: 50)
deployment_idYesDeployment ID returned by run_workload or deploy_model

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden; it does communicate that this is a state-read plus log-fetch and that leaving a deployment running accrues billing. It omits what 'state' values are possible, whether this call itself has side effects or cost, and any latency or retry characteristics.

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?

Three short sentences, no waste, with purpose front-loaded before the sequencing hint and the billing warning. Every sentence carries actionable information.

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?

There is no output schema, so the description should explain what comes back; it names 'state' and 'log lines' but does not enumerate states or indicate log formatting. For a status-check tool this leaves a meaningful gap, though the lifecycle guidance is solid.

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?

deployment_id provenance is added beyond the schema ('returned by run_workload or deploy_model'). log_lines is left entirely to the schema, which already documents default 50 and the 0-500 range at 100% 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?

States a specific verb and resource: check state of one Badgr deployment plus fetch recent log lines. It ties itself to the run_workload/deploy_model lifecycle, though it does not distinguish itself from siblings badgr_status or badgr_logs, which appear to overlap.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger ('use after run_workload or deploy_model, with the returned deployment_id') and a follow-up action ('call badgr_down when finished so billing stops'). No exclusion versus badgr_status/badgr_logs is stated, so it stops short of full alternative routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.