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vktr93
by vktr93

prepare_text_fixes

Prepares text fixes for a completed review run so requested edits can be retrieved; use when the user asks for fixes.

Instructions

Prepare the text fixes of a completed review (uses 1 RM token; asking again for the same review is free). Only call this when the user asked for the fixes. Then call get_text_fixes to receive the edits.

Args: run_id: The Run ID of a completed review.

Input Schema

NameRequiredDescriptionDefault
run_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.2.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description still discloses useful cost behavior: it uses 1 RM token and repeat requests for the same review are free. However, it does not explain permissions, whether preparation creates persistent state, what happens if the review is incomplete, or other side effects, so important behavioral context is missing.

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 short and front-loaded with purpose and cost, followed by constraints, the next tool, and the single argument. Every sentence adds operational value, and the Args section is compact for a one-parameter tool.

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

Completeness4/5

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

For a one-parameter tool with an output schema, the description covers the key prerequisite (completed review), cost, and the follow-up retrieval step. It omits how to obtain the run_id and error behavior for incomplete reviews, but the output schema removes the need to explain return values.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate; it identifies run_id as the Run ID of a completed review, adding a useful prerequisite. It does not specify the expected format or where an agent should obtain the run_id, so compensation is only partial.

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 states a specific action (prepare text fixes) on a specific resource (a completed review) and distinguishes this tool from the sibling get_text_fixes by describing the workflow: prepare first, then retrieve the edits with get_text_fixes. An agent can identify the tool's role 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 Guidelines5/5

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

It explicitly limits use to cases where the user asked for the fixes and names the required follow-up tool get_text_fixes. This provides clear when-to-use guidance and a concrete next step, leaving little to inference.

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