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diagnose_prompt

Read a field and return a list of specific improvement suggestions (string array).

field: same values as improve_prompt. current = the text to diagnose (required — an empty text
has nothing to diagnose). Typical flow: diagnose_prompt first, pick the suggestions you want,
then fold them into improve_prompt's hint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldYes
contextNo
currentYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior3/5

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

No annotations exist, so the description must bear the burden of behavioral disclosure. It does state the tool is a read operation returning a list and notes the empty-input edge case, but it does not mention side effects, error conditions, or the role of the context parameter. This is moderate disclosure but not comprehensive.

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 appropriately concise: the first sentence states the core purpose, and the second paragraph adds parameter explanations and workflow. It is front-loaded, uses bold for emphasis, and every sentence contributes value without redundancy.

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?

The description covers the main workflow and two of three parameters, but it omits the context parameter and does not enumerate valid field values, relying on a reference to improve_prompt. Given no annotations and no output schema, these gaps make the description adequate but not fully complete for correct invocation in all cases.

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?

With 0% schema description coverage, the description compensates by explaining the field parameter (same values as improve_prompt) and the current parameter (the text to diagnose, required, empty not useful). However, the context parameter is completely omitted, and field values are only given by reference rather than enumerated, so it is not fully self-contained.

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 clearly states that the tool reads a field and returns a string array of specific improvement suggestions. It distinguishes itself from improve_prompt by positioning it as a diagnostic precursor, and the verb 'read' plus the explicit output type make the purpose unambiguous.

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

Usage Guidelines5/5

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

The description explicitly provides a typical flow: use diagnose_prompt first, pick suggestions, then fold them into improve_prompt's hint. This references the sibling tool improve_prompt and establishes when to use this tool. It also gives a precondition (non-empty current text), offering clear usage guidance.

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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