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HydroStar MCP

HydroStar MCP

by lightninnng
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

make_reference_case

Read-only

Creates a reviewable typed recipe from public scalar references without writing data, returning source comparisons and a full plan for downstream preparation and benchmarking.

Instructions

Create a reviewable typed recipe from public scalar references, without writes.

Returns source comparison and a full plan. Generate/prepare the returned config using the normal gates; benchmark equivalence is evaluated separately.

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": {
      "form_id": "<form_id>"
    },
    "name": "make_reference_case"
  }
}

Input Schema

NameRequiredDescriptionDefault
form_idYes
projectNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

B3.2/5.0
Behavior3/5

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

The readOnlyHint annotation is consistent with the description's "without writes" claim, and the description adds useful workflow context: it returns source comparison and a full plan, with generation/preparation handled by normal gates. With the annotation already covering the safety profile, this is solid but not especially rich behavioral disclosure.

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 description is short, front-loaded, and appropriately sized for the tool. The phrasing "normal gates" is a little vague, but overall there is little wasted text.

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?

It covers the read-only behavior and the returned plan, and an output schema exists to carry return details. The major gap is parameter semantics: with 0% schema description coverage and no parameter discussion in the description, the agent lacks enough context to understand what form_id and project mean.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain either parameter. It never mentions form_id or project, so the agent gets no meaning beyond the raw schema types for the two parameters.

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 states a specific verb and resource -- "Create a reviewable typed recipe" -- and scopes it to "public scalar references, without writes." It is clear about what the tool produces, but it does not differentiate itself from siblings such as plan_case, generate_inputs, or prepare_case.

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?

The description implies usage by saying to generate/prepare the returned config using the normal gates and that benchmark equivalence is evaluated separately. However, it gives no explicit when-to-use guidance versus alternatives and no exclusions.

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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