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 mentions the tool analyzes codebases and answers questions, but lacks details on permissions, rate limits, response format, or error handling. For a tool with 4 parameters and no output schema, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, consisting of two clear sentences that directly state the tool's function. There's no wasted verbiage, and it efficiently communicates the core purpose. However, it could be slightly more structured by explicitly mentioning key parameters or use cases.

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

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

Given the tool's complexity (4 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the relationship between codebase_path and directory_path, what kind of questions are supported, or what the output looks like. For a code analysis tool with multiple input options, more context is needed to guide effective usage.

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

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

The description doesn't explicitly discuss parameters, but schema description coverage is 100%, providing clear documentation for all 4 parameters. The description implies the tool answers questions about codebases, which aligns with the 'question' parameter. However, it doesn't add meaningful context beyond what the schema already covers, such as how codebase_path and directory_path interact.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Analyzes codebases using Repomix and Gemini 2.5 Pro. Answers questions about code structure, logic, and potential improvements.' It specifies the action (analyzes/answers), resource (codebases), and technology used (Repomix and Gemini 2.5 Pro). However, it doesn't explicitly differentiate from sibling tools like gemini_fileops, gemini_reason, or gemini_search, which likely have related but distinct functions.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions analyzing codebases and answering questions, but doesn't specify use cases, prerequisites, or exclusions. Without context, it's unclear how this differs from sibling tools, leaving the agent to guess based on tool names alone.

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

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 mentions model selection (Flash-Lite vs. 1.5 Pro) which adds useful context about performance characteristics, but fails to disclose critical behavioral traits such as whether operations are read-only or destructive, authentication requirements, rate limits, error handling, or output format. For a file operation tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences that are front-loaded with the core purpose and usage context. Every sentence earns its place by conveying essential information without redundancy or fluff.

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

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

Given the tool's moderate complexity (5 parameters, file operations), no annotations, and no output schema, the description is incomplete. It covers the basic purpose and usage context but lacks critical behavioral details (e.g., mutation effects, error handling) and output information. The schema handles parameters well, but the description should compensate more for the missing annotations and output schema.

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

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 all 5 parameters thoroughly. The description adds minimal value beyond the schema by implying the 'operation' parameter corresponds to 'summarization, extraction, or basic analysis' and hinting at model selection logic, but doesn't provide additional syntax, format details, or constraints. 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'performs efficient operations on files' and specifies the types of files (text, PDF, images) and operations (summarization, extraction, basic analysis). It distinguishes from siblings by focusing on file operations rather than code, reasoning, or search. However, it doesn't specify the exact verb+resource combination beyond 'operations on files' which is slightly broad.

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

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

The description provides clear context for when to use this tool ('for summarization, extraction, or basic analysis') and mentions model selection criteria (Flash-Lite or 1.5 Pro for large files). It doesn't explicitly state when not to use it or name alternatives among siblings, but the operational focus implies differentiation from gemini_code, gemini_reason, and gemini_search.

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

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 describes the step-by-step reasoning approach and mentions the 'transparent reasoning process,' which adds value beyond basic functionality. However, it doesn't cover important behavioral aspects like rate limits, authentication requirements, error handling, or what the output looks like (though there's no output schema).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two well-structured sentences. The first sentence states the core functionality, and the second provides usage guidance. Every word earns its place with no redundancy or unnecessary information.

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

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

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description provides adequate but incomplete coverage. It explains the purpose and usage context well but lacks details about behavioral characteristics, output format, and error handling. With no output schema, the description should ideally mention what kind of response to expect.

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

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

The schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('solves complex problems with step-by-step reasoning') and resources ('using Gemini 2.0 Flash Thinking'). It distinguishes from siblings by specifying it's for 'math and science problems, coding challenges, and tasks requiring transparent reasoning process' rather than code execution, file operations, or search.

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

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

The description provides clear context about when to use this tool ('Best for math and science problems, coding challenges, and tasks requiring transparent reasoning process'), which implicitly suggests alternatives for other types of tasks. However, it doesn't explicitly name sibling tools or state when not to use this tool, keeping it at a 4 rather than a 5.

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

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 mentions the technology ('Gemini 2.0 Flash and Google Search') and use cases, but lacks details on rate limits, authentication needs, response format, or potential side effects. It adequately describes the core function but misses operational context that would help an agent invoke it effectively.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by usage guidance. Every sentence earns its place by adding value without redundancy. It's appropriately sized for a tool with clear functionality and good schema coverage.

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

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

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose and usage well but lacks details on behavioral aspects like response format or error handling. With no output schema, it could benefit from mentioning what the tool returns, but the clarity of purpose compensates somewhat.

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

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 all parameters thoroughly. The description adds no parameter-specific information beyond implying the 'query' parameter's purpose through context. This meets the baseline of 3 since the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Generates responses') and resources ('using Gemini 2.0 Flash and Google Search'), and distinguishes it from siblings by specifying its domain ('general knowledge questions, fact-checking, and information retrieval'). It goes beyond a tautology by explaining the technology stack and use cases.

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

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

The description explicitly states when to use this tool ('Best for general knowledge questions, fact-checking, and information retrieval'), which implicitly suggests alternatives (e.g., use gemini_code for coding tasks, gemini_reason for reasoning-heavy queries). This provides clear context for selection among siblings without needing explicit exclusions.

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