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Platano78

Smart-AI-Bridge

analyze_file

Analyze any file by providing a file path and a question. The tool uses local AI to generate structured findings on security, bugs, performance, or architecture, saving tokens by not sending full content to external models.

Instructions

Local LLM File Analysis - Reads and analyzes files using local LLM. Claude never sees full file content, only structured findings. Token savings: 2000+ to ~150 tokens per file.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filePathYesPath to the file to analyze
questionYesQuestion about the file (e.g., "What are the security vulnerabilities?")
optionsNo
Behavior4/5

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

Discloses important behavioral traits: uses local LLM, keeps file content from Claude, and provides token savings. With no annotations, it carries the full burden and does well, though it omits whether the tool is read-only or has side effects.

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?

Three sentences with clear front-loading of the tool's purpose. No unnecessary words, efficient and direct.

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?

Despite good purpose clarity, the description lacks details on return values (structured findings format), when not to use, and handling of nested options. For a tool with no output schema and nested parameters, more completeness is expected.

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 67%, so the schema already describes most parameters. The description adds no additional meaning beyond the schema, giving no extra context for parameters like options or question.

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?

Clearly states the tool reads and analyzes files using local LLM, highlighting privacy (Claude never sees full content) and token savings. Differentiates from siblings like batch_analyze and review by emphasizing single-file analysis with local processing.

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?

No explicit guidance on when to use this tool versus alternatives like batch_analyze or review. Usage is only implied through the description, lacking when-not or context for alternatives.

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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