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PyModel
by PyModel

analyze_files

Analyze large files or multiple files without loading them into context. Delegates to an external CLI to answer queries about logs, code, or cross-file comparisons.

Instructions

Delegate file analysis to the Antigravity CLI (Gemini) instead of reading files yourself. USE THIS whenever a file is large (>200 lines) or the task spans more than 3 files: logs, database dumps, generated code, cross-file reviews, comparisons. The files never enter your context — only the answer does.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoAbsolute path to the working directory / project root. Defaults to the server's cwd.
filesYesFile paths to analyze (relative to cwd or absolute).
modelNoOverride the model (exact name from `agy models`, e.g. "Gemini 3.1 Pro (High)"). Normally omit — the tool routes automatically.
questionYesWhat you want to know about these files.
Install Server

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds a key behavioral trait: 'The files never enter your context — only the answer does,' which is crucial for understanding how the tool operates (offloading analysis to an external CLI). However, it does not mention any potential side effects, prerequisites, or failure modes, which would elevate it to a 5.

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 two sentences long. The first sentence states the core action, and the second provides concrete usage triggers with examples. Every word is purposeful, no filler, and the key information is front-loaded. This is exemplary conciseness.

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?

The description covers purpose, usage criteria, and a key behavioral trait (context isolation). With a 100% schema-covered parameter set and no output schema, it is nearly complete. It could be slightly enhanced by explicitly stating the answer format (e.g., 'returns a text summary'), but the phrase 'only the answer does' implies a return value. Overall, the tool is well-contextualized.

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 100%, so each parameter already has a clear description. The tool description adds no additional parameter-level detail (e.g., format, constraints) beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 the tool's purpose: 'Delegate file analysis to the Antigravity CLI (Gemini) instead of reading files yourself.' This uses a specific verb ('delegate'), a resource ('file analysis'), and explicitly distinguishes itself from direct file reading, which aligns with the sibling tools being web/analysis-oriented. It is unambiguous and unique.

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?

Explicit usage criteria are provided: 'USE THIS whenever a file is large (>200 lines) or the task spans more than 3 files: logs, database dumps, generated code, cross-file reviews, comparisons.' It also implies when not to use it ('instead of reading files yourself'), giving clear when/when-not guidance. This is a model of usage guideline clarity.

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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