Piping Toolset Engineering Data

Worked example for a skill

get_fixture_example

Read-onlyIdempotent

Return one worked example case for a Piping Toolset plugin skill: the inputs, the expected outputs and the pass/fail status from the hand-checked calculation fixtures, with case names removed. case selects among the skill's cases (0-based; the result gives the count).

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": {
      "skill": "<skill>"
    },
    "name": "get_fixture_example"
  }
}

Input Schema

Name	Required	Description	Default
`case`	No	0-based case index (default 0)
`skill`	Yes	Plugin skill slug (current names first; 0.9.x names stay accepted)

Output Schema

Name	Required	Description	Default
`tol`	Yes
`input`	Yes
`skill`	Yes
`expect`	Yes
`module`	Yes
`status`	Yes
`case_count`	Yes
`case_index`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, closed-world, non-destructive, so the bar is lower. The description nonetheless adds behavior beyond them: case names are stripped from the output (anonymization), pass/fail status is included, and the response self-reports case count so the agent can enumerate safely.

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?

The payload is described first and the parameter note follows, which is the right order. It is dense and mostly waste-free, though the trailing parenthetical about 0-based indexing and count spills into a slightly awkward fragment.

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?

With an output schema present and 100% schema coverage, the description need not document return shapes or parameter formats. It still adds the two things the structured fields cannot: anonymization and case-count discoverability, which is everything an agent needs to call it correctly.

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?

The schema already documents both parameters at 100% coverage, but the description adds non-obvious meaning: that `case` is 0-based and that the real number of cases is discoverable from the result rather than from the schema's max of 500. That is genuinely useful framing the schema does not convey.

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?

The description names a specific verb and resource ('Return one worked example case for a Piping Toolset plugin skill') and enumerates the payload (inputs, expected outputs, pass/fail status from hand-checked fixtures). An agent can distinguish this fixture-retrieval tool from the dataset/metadata siblings without opening the schema.

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 only implied: the agent infers it should call this when it needs a concrete worked example for one of the enumerated skills. There is no explicit when-to-use/when-not-to-use guidance and no alternative tool is named, though the 'skill' enum in the schema effectively scopes the call.

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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