Maglev Parts

Check a bench test result

check_test_result

Read-only

Check a bench test result for a joint actuator against the result format (/api/test/result.schema.json, template at /api/test/result-template.json) and compare the measured values with the part's datasheet in the catalog: peak torque, continuous torque, backlash, Kv, Kt. Returns errors, warnings and each measured-vs-datasheet delta. Nothing is stored.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "result": "<result>"
    },
    "name": "check_test_result"
  }
}

Input Schema

Name	Required	Description	Default
`result`	Yes	One result in maglev.test-result/0.1 format

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, but the description adds real context beyond them: it confirms nothing is persisted and it describes the response shape (errors, warnings, measured-vs-datasheet deltas) in the absence of an output schema. It does not mention whether the datasheet comparison depends on a catalog part being resolvable, which is the one behavioral gap.

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?

Two sentences, front-loaded with the action, and the metric list is compact. The inline file paths interrupt the flow slightly but each carries information an agent would otherwise have to hunt for.

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

Completeness5/5

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

For a single-parameter, read-only tool with no output schema, the description covers the input format (via schema and template pointers) and the return values (errors, warnings, per-metric deltas). Nothing essential to correct invocation appears to be missing.

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?

Schema coverage is 100% and there is only one parameter, so the baseline is 3. The description earns more by pointing to the authoritative format schema (/api/test/result.schema.json) and a template file for the nested result object, which is genuinely useful for constructing a deeply nested argument.

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

Purpose5/5

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

States a specific verb and resource ('Check a bench test result for a joint actuator') plus the two things it does: schema validation and comparison of measured values against the catalog datasheet. The enumerated metrics (peak torque, continuous torque, backlash, Kv, Kt) make it unmistakably distinct from a sibling like check_manufacturability.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: an agent can infer this is the tool for validating a test result object before using it, and 'Nothing is stored' signals a safe pre-check. However, no explicit when-to-use versus alternatives (e.g. get_test_protocol or check_manufacturability) is provided, and no preconditions are named.

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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