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 useful context (1M token context, input flexibility) but does not state whether the operation is read-only, the runtime expectations, or system accesses required. It is adequate but lacks rich behavioral depth.

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 three focused sentences with no redundant wording. It front-loads the core action and then efficiently expands on capability and outcomes, making every sentence earn its place.

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?

This is a complex tool with 10 parameters, no output schema, and no annotations. The description covers the primary purpose, input modes, and the types of analysis results returned, which is sufficient for high-level agent decision-making. Some details are left to the schema, but the description is not incomplete enough to hinder selection or invocation.

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 every parameter is already documented. The description loosely maps to parameters via 'directory path, file paths, or file contents' and analysis areas, but it does not add meaning beyond what the schema provides. Baseline 3 is appropriate.

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 a specific verb ('Analyze') and resource ('entire codebase'), and enumerates the analysis outcomes (architecture, patterns, security, performance, dependencies). This distinguishes it from sibling tools like gemini_analyze_content or gemini_search, which target different input types or tasks.

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 implies when to use this tool (when codebase analysis is needed) and mentions supported input modes (directory path, file paths, file contents), providing clear context. However, it does not explicitly name alternatives or exclusions relative to sibling tools, so it stops short of 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, the description carries the burden of behavioral disclosure. It adds value by mentioning auto-detection and parallel call support, but it does not describe return structures, limitations, or any side effects. This is acceptable for a read-like analysis tool but not comprehensive.

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 compact and front-loaded, with the first sentence immediately stating the core function. The additional tip about parallel calls is useful and does not add fluff. Every sentence serves a purpose.

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?

The tool has 8 parameters, no annotations, and no output schema, so the description needs to cover a lot. It handles the main functionality and input methods, but omits output format behavior or return value expectations. Given the complexity, this is a clear gap, making the description adequate but not complete.

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 coverage is 100%, so the baseline is 3. The description adds meaning by consolidating the task list, explaining the file path vs. direct content input, and highlighting auto-detection of type and language, which complements the schema. This goes slightly beyond the schema's individual descriptions.

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 analyzes code, documents, or data and lists specific tasks (summarization, code review, etc.), making the primary purpose evident. However, it does not explicitly distinguish this from the sibling tool gemini_analyze_codebase, so the differentiation is not fully articulated.

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 usage context by mentioning support for file path or direct content input and auto-detection of content type and language. It also includes a practical tip about parallel calls. However, it lacks explicit 'when not to use' guidance or alternative tool mentions, so it stops short of full usage guidelines.

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. It does disclose key behaviors: it produces multiple ideas with pros/cons, includes feasibility assessment, and reads project context files. However, it does not mention potential side effects like file reading permissions, external model calls, or any limitations/errors, leaving gaps 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 two sentences, front-loaded with the primary action, and every phrase adds value (creative ideas, pros/cons, feasibility, project context). There is no 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?

The description adequately covers the core purpose and the standout feature (context files), and the schema fills parameter details. However, with no output schema and no annotations, it leaves out the output format structure and any caveats about file reading or model behavior. For a 7-parameter tool, it is sufficient but not exhaustive.

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% (baseline 3). The description adds meaning by explaining that context files are used to tailor ideas to the project, and that output includes pros/cons and feasibility assessment—context not present in the schema. This enhances understanding of contextFilePath and count.

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 uses a specific verb ('Generate creative ideas and solutions') and clearly differentiates from sibling tools by emphasizing brainstorming with pros/cons and feasibility assessment. It also mentions the unique ability to read project context files, making its purpose unmistakable.

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 implicitly conveys when to use it ('Generate creative ideas and solutions', 'fit your project') and highlights the context-file feature as a specific use case. However, it does not explicitly state when not to use it or mention sibling tools as alternatives, which would earn 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 only restates the purpose and examples, without mentioning whether the operation is read-only, any rate limits, or limitations. It also does not disclose output behavior beyond what the schema already indicates. This is a notable gap for a tool that might be used to query potentially sensitive visual content.

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 extremely concise—two sentences that front-load the primary function and then provide examples. Every sentence earns its place, with no wasteful or redundant text.

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, 2 required, no output schema) and lack of annotations, the description is adequate but not fully complete. It explains what the tool does and gives use cases, but omits details about output format expectations or any prerequisites. Since all parameter descriptions are present in the schema, the description is minimally viable but leaves the agent with some uncertainty about the return value structure.

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 coverage is 100%, so the baseline is 3. The description aligns with the prompt and images parameters by mentioning 'images + text' and 'natural language questions,' and provides concrete examples. However, it does not add any extra meaning about model selection, context, or outputFormat beyond what the schema already documents.

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 function with a specific verb ('Query') and resource ('images + text for multimodal understanding'). It distinguishes itself from siblings by focusing on visual content analysis (designs, diagrams, screenshots), unlike gemini_analyze_content or gemini_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?

Provides clear context by listing example use cases (designs, diagrams, screenshots) and emphasizes 'natural language questions' for visual understanding. However, it does not explicitly state when not to use this tool or name alternatives, so it falls short of a perfect 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 explains that results include source citations, that groundingMetadata contains source URLs, that thinkingLevel controls reasoning depth, and that parallel calls are supported. It does not mention potential errors, rate limits, or fallback behavior, but the disclosed traits are substantial and genuinely informative.

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 well-structured with a short intro, a 'Features' section, and a 'Usage Tips' section. Every line provides useful information without fluff. The parallel calls note is emphasized clearly. It is appropriately concise given the number of options and behaviors covered.

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?

There is no output schema, so the description should explain what the agent can expect in return. It does mention source citations and groundingMetadata, which gives a partial picture of the output. However, it lacks details on the exact structure of JSON output or how results are ordered/limited. For a search tool with this many options, the description is fairly complete but not exhaustive.

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 coverage is 100%, so the baseline is 3. The description adds value beyond the schema by advising when to use thinkingLevel 'low' vs 'high' and outputFormat 'json', which directly informs parameter choice. This goes beyond simply restating the schema and helps the agent select appropriate values.

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 opens with a specific verb+resource: 'Search the web using Gemini's built-in Google Search grounding.' This clearly states what the tool does and distinguishes it from sibling tools (multimodal_query, analyze_content, analyze_codebase, brainstorm) which are not web search. The features list further clarifies its purpose.

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 explicit 'Ideal for' guidance (current events, latest documentation, real-time data, fact-checking) and practical usage tips for thinkingLevel and outputFormat. It does not explicitly name alternatives or when-not-to-use, so it stops short of a 5, but the context is clear enough for an agent to select this tool appropriately.

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