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rnd-pro
by rnd-pro

ai-tools__analyze_project

Analyze any project structure by providing a description, and receive targeted insights on architecture, performance, security, or other focus areas.

Instructions

[AI Code Analysis] Analyzes project structure and provides insights

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNoWhat aspect to focus on (architecture, performance, security, etc.)general
project_descriptionYesDescription of the project or files to analyze

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.3

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It conveys a non-destructive analysis activity, but does not explicitly state that files are not modified, what access it performs, or what kind of insights are returned. This is minimal transparency for an unannotated tool.

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 a single, front-loaded sentence with no filler. The '[AI Code Analysis]' prefix and the core verb-object structure make the tool's purpose immediately visible. Every part earns its place.

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

Completeness2/5

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

With no output schema and no annotations, the description should provide more context about what 'insights' means, what the tool returns, and how the analysis is conducted. The current description is too thin for an agent to know what to expect from invocation or how to interpret the result.

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 the schema already documents both parameters adequately. The description does not add meaningful detail about how 'project_description' or 'focus' should be used beyond what the schema provides, so the baseline of 3 is appropriate.

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 states a clear action and object: 'Analyzes project structure and provides insights.' This distinguishes it from code-specific siblings like explain_code, review_code, and fix_code by implying a higher-level, project-wide scope. However, 'insights' remains vague, so it stops short of a perfect 5.

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?

Usage context is implied rather than stated: the name and description suggest it is for high-level project analysis, while siblings target code explanation, review, and modification. There is no explicit guidance on when to choose this over alternatives or when not to use it.

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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