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split_plan

Generate split suggestions for oversized code files by analyzing responsibility and cohesion across 13 programming languages to improve code structure.

Instructions

Concrete split suggestions for an oversized file, grouped by responsibility and cohesion. Works with any supported language.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions output grouping ('by responsibility and cohesion') and language support, but lacks critical behavioral details: whether this is a read-only analysis or performs modifications, what permissions are needed, expected runtime or resource usage, and how suggestions are presented (e.g., as text, structured data). For a tool with one parameter and an output schema, this leaves significant gaps in understanding its operation.

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 highly concise and well-structured in two sentences. The first sentence front-loads the core purpose and output grouping, while the second adds scope without redundancy. Every word earns its place, with no wasted text or vague phrasing, making it efficient for an agent to parse.

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?

Given one parameter with 0% schema coverage and an output schema (which handles return values), the description is minimally complete. It covers the tool's purpose and scope but lacks behavioral transparency and usage guidelines. For a tool that analyzes files, more context on input constraints (e.g., file types, size limits) and output interpretation would be beneficial, but the output schema mitigates some gaps.

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 adds context that the parameter 'file_path' refers to 'an oversized file' and implies it should be in a 'supported language', giving basic semantics. However, it doesn't specify file format expectations, size thresholds for 'oversized', or path requirements. With one parameter, the baseline is 4, but the description provides only partial compensation, resulting in an adequate but incomplete understanding.

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 clearly states the tool's purpose: 'Concrete split suggestions for an oversized file' specifies the action (providing split suggestions) and resource (oversized file). It adds 'grouped by responsibility and cohesion' which clarifies the output structure, and 'Works with any supported language' defines scope. However, it doesn't explicitly distinguish this from sibling tools like 'bloat_report' or 'generate', which might also analyze files.

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

Usage Guidelines2/5

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

The description provides minimal usage guidance. It implies use when dealing with 'an oversized file' but doesn't specify when to choose this over alternatives like 'bloat_report' (which might identify bloat) or 'generate' (which might create code). No explicit when-not-to-use criteria or prerequisites are mentioned, leaving the agent to infer context from tool names alone.

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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