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

No annotations are provided, so the description carries full burden. It indicates the tool performs analysis and returns recommendations but does not disclose whether it is read-only, its resource usage, or any side effects. The behavioral traits are insufficiently transparent.

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 a single, well-structured sentence that front-loads the core purpose. Every word contributes meaning, with no redundancy or filler.

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 has only two parameters and no output schema, the description covers input type, optional analysis focus, and output (insights/recommendations). It lacks details about output format or constraints but is mostly complete for its complexity.

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%, with clear descriptions for both parameters (imageUrl and analysisFocus) in the schema. The tool description adds no further value beyond the schema, achieving baseline adequacy.

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 reads charts, dashboards, and statistical visualizations to surface insights, trends, patterns, and anomalies with actionable recommendations. It differentiates from siblings like general 'image_analysis' by specifying the input type (data visualizations) and output (actionable insights).

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 usage for data visualizations but does not explicitly state when to use this tool versus alternatives like 'image_analysis' or 'understand_technical_diagram'. No exclusions or prerequisites are mentioned, leaving room for ambiguity.

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?

No annotations are provided, so the description carries full burden. It states the tool analyzes errors and proposes fixes, but does not disclose processing specifics (e.g., required image formats, handling of multiple errors, or API dependencies). Adequate but lacks deeper behavioral detail.

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 a single, front-loaded sentence that efficiently conveys action, input types, and output. Every word contributes, with no redundancy or filler.

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?

With no output schema, the description appropriately mentions 'propose actionable fixes with root cause analysis' to indicate output. It could be more specific about the format or structure of the analysis, but it is largely complete for a diagnostic tool.

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% (both parameters documented). The tool description does not add new information about parameters beyond the schema; it only reiterates the general purpose. Baseline is 3, and the description does not elevate it.

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: analyzing error screenshots (build errors, runtime errors, stack traces) and proposing actionable fixes with root cause analysis. It specifies the input type and output, distinguishing it from sibling tools that handle different visual data (e.g., data visualizations, diagrams).

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: when an error screenshot needs diagnosis and fixes. However, it does not explicitly mention when not to use or provide alternatives among siblings. The context is clear but could be improved with exclusions.

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 full behavioral burden. It discloses that extracted text preserves original structure, which is useful. However, it lacks details on limitations (e.g., image size, language support, accuracy) and fails to mention any destructive or idempotent behavior.

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 with three short sentences. The first sentence states the primary action, the second adds optimization contexts, and the third describes the output. Every sentence provides unique value without redundancy.

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 simplicity (2 parameters, no output schema), the description covers purpose, optimization contexts, and return value preservation. It is largely complete, though it could mention prerequisites like supported image formats or error handling for unsupported inputs.

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 already describes both parameters with 100% coverage. The description adds value by explaining that the 'context' parameter is optimized for specific content types (terminal, code, doc, all), which maps directly to the enum values and gives semantic meaning beyond the schema.

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 extracts text from screenshots, and specifies optimization for terminals, code editors, documents, and general content. This distinguishes it from sibling tools like 'diagnose_error_screenshot' or 'image_analysis', which have different purposes.

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 use cases through optimization contexts but does not explicitly state when to use this tool versus alternatives like 'diagnose_error_screenshot' or 'ui_diff_check'. No when-not-to-use guidance is provided, requiring the agent to infer.

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?

The description discloses the output includes image content, text, colors, layout, and context clues, which is helpful. However, with no annotations, it lacks details on limitations, error handling, or whether it handles all image types. Some transparency 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?

Two concise sentences, front-loaded with action and followed by output summary. No wasted words; efficient and clear.

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 no output schema or annotations, the description adequately covers the tool's purpose and output. It explains what the tool returns and accepts. Could include more detail on edge cases but sufficient for a general analysis tool.

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 description adds no new parameter semantics. It repeats the general purpose but does not elaborate on parameter syntax or constraints beyond what the schema already provides.

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 any image with a general vision model and returns a detailed description. It is specific about verb and resource, but does not explicitly differentiate from sibling tools that are more specialized.

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?

No guidance on when to use this tool versus alternatives. The description does not mention context for choosing image_analysis over specialized siblings like diagnose_error_screenshot or extract_text_from_screenshot.

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?

No annotations are provided, so the description carries the burden. It describes the tool as a comparison operation ('Compare two UI screenshots'), implying a read-only, non-destructive behavior. It does not mention side effects, but for a comparison tool this is adequate. No contradictions.

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?

A single sentence that efficiently communicates the tool's purpose without wasted words. It is front-loaded with the action 'Compare' and specifies the exact outputs. Ideal conciseness.

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 explains the tool's purpose but does not describe the output format or return value. Since no output schema is provided, the description should hint at what the tool returns (e.g., a list of differences with coordinates). This omission reduces completeness.

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% with clear parameter descriptions. The tool description adds only context ('design vs implementation') beyond what the schema already provides. Baseline 3 is appropriate as the schema already handles parameter documentation.

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: comparing two UI screenshots for visual differences, layout drift, style inconsistencies, missing elements, and typography discrepancies. It uses specific verbs and a clear resource 'UI screenshots', and distinguishes from siblings by focusing specifically on UI diffing.

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 usage when comparing design vs implementation screenshots, but does not explicitly state when to use this tool over siblings (e.g., 'image_analysis' for general comparison). No 'when not to use' or alternative recommendations are provided.

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 bears full responsibility for behavioral transparency. It discloses the core function and output types but omits details like file size limits, supported image formats, or whether the tool modifies anything. The description is straightforward but lacks depth on behaviors.

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 a single, front-loaded sentence with no redundant information. Every word contributes to understanding the tool's purpose and outputs.

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 has only two parameters, no output schema, and no annotations, the description provides adequate context for typical usage. It could mention output format or limitations, but overall it is sufficient for an agent to select and invoke the tool.

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 already describes both parameters and covers 100%. The description adds value by explaining what each outputType value produces ('code' means HTML/CSS/React, 'prompt' means AI image gen prompt, etc.), which goes beyond the enum list.

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 converts UI screenshots into structured deliverables, listing four distinct output types (code, prompt, spec, description). This distinguishes it from sibling tools like analyze_data_visualization or diagnose_error_screenshot, which target specific 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 implies usage for converting UI screenshots but provides no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use or which sibling handles other types of screenshots.

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?

No annotations are provided, so the description must fully convey behavioral traits. It mentions returning 'structured analysis of components, relationships, design patterns, and improvement suggestions,' but lacks details on limitations, error handling, or processing behavior.

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?

Two clear, front-loaded sentences with no redundant information. Every word adds value.

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 (handling multiple diagram types), the description omits output format details. No output schema exists, so the description should clarify what 'structured analysis' means. Sibling tools provide context, but completeness is moderate.

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% with descriptions for both parameters. The description adds no extra meaning beyond listing diagram types, which aligns with the enum. No further semantic value is provided.

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?

Description clearly states the tool interprets various technical diagrams (architecture, flowcharts, UML, etc.) and returns structured analysis. This distinguishes it from sibling tools like analyze_data_visualization or extract_text_from_screenshot, which handle different image types.

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?

Description lists diagram types but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions. The context implies use for technical diagrams, but explicit guidance is missing.

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?

No annotations are provided, so the description carries the burden. It discloses file size limit and format support, but does not explain frame-sampling behavior (maxFrames, fps) or output format. Behavioral traits beyond basic constraints are missing.

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?

Two sentences, efficiently front-loaded with the core purpose and constraints. No wasted words; every part adds value.

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 covers the main purpose and file constraints, but given 4 parameters and no output schema, it omits explanation of frame-sampling parameters (maxFrames, fps) and how the analysis produces output. It is adequate but not fully informative.

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 baseline is 3. The description adds the file size constraint (≤8MB) beyond the schema, but does not elaborate on prompt usage or the frame-sampling mode. Minimal additional meaning is provided.

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 verb (inspect), resource (videos), and scope (describe scenes, detect events, answer questions). It specifies supported formats and file size limit, distinguishing from image-focused sibling tools like image_analysis.

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 usage for video analysis but does not provide explicit guidance on when to use this tool versus alternatives (e.g., image_analysis for static images). No exclusions or when-not-to-use context is given.

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