Server Quality Checklist
Latest release: v1.0.0
- Disambiguation3/5
Most tools have distinct purposes (e.g., analyze_video vs. summarize_video), but some overlap exists: analyze_video and video_qa both handle video Q&A, and extract_video_text could be seen as a subset of analyze_video's capabilities. The descriptions help differentiate, but agents might still misselect between closely related tools.
Naming Consistency4/5Tools follow a consistent verb_noun pattern (e.g., analyze_image, check_endpoint_status, list_capabilities), with only minor deviations: compare_video_frames uses 'compare' instead of 'analyze' or 'extract', but it's still readable and fits the pattern. Overall, the naming is predictable and clear.
Tool Count5/5With 8 tools, the count is well-scoped for a video understanding server. Each tool serves a specific function (e.g., analysis, summarization, text extraction), and there are no redundant or trivial additions. This number allows comprehensive coverage without being overwhelming.
Completeness4/5The toolset covers core video understanding tasks well: analysis, summarization, Q&A, text extraction, and comparison. Minor gaps exist, such as no explicit tool for video editing or metadata retrieval, but agents can work around these with the provided tools. The domain is adequately covered for most use cases.
Average 3.6/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
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Tool Scores
- 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 describes the output styles but lacks critical details such as whether the tool requires authentication, handles errors, has rate limits, or what the return format looks like (e.g., text, structured data). For a tool with no annotations, 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.
Conciseness5/5Is 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 a clear, bulleted list of style options. Every sentence earns its place by providing essential information without redundancy, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (summarizing video content) and lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like error handling, performance expectations, or output format, leaving gaps that could hinder an AI agent's ability to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 ('video_url' and 'style') with descriptions. The description adds value by elaborating on the 'style' parameter with specific definitions for 'brief', 'standard', and 'detailed', but doesn't provide additional context beyond what the schema offers for 'video_url'. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Generate a summary of a video' with a specific verb ('Generate') and resource ('video'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_video' or 'extract_video_text', which might also involve video processing but with different outputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 like 'analyze_video' or 'video_qa'. It lists style options but doesn't explain scenarios where one style is preferable over another, nor does it mention any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
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 what the tool does but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires authentication, potential rate limits, or what format the capabilities are returned in. This leaves significant gaps for an agent to understand how to interact with it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'capabilities' means in this context (e.g., supported video formats, analysis types), how results are structured, or any prerequisites. For a tool in a server with multiple analysis siblings, more context is needed to guide proper use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to compensate for any parameter documentation gaps. A baseline of 4 is appropriate since there are no parameters to explain beyond what the schema already covers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('capabilities of this video understanding server'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings (like 'check_endpoint_status' which might also provide server information), so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 doesn't mention when this tool is appropriate (e.g., for discovering available features before analysis) or when to choose sibling tools like 'check_endpoint_status' for different server information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it states what the tool returns ('configured endpoint URLs and connection status'), it doesn't describe important behavioral aspects: whether this requires authentication, what format the status information takes, if there are rate limits, whether it performs active connection testing or returns cached data, or what happens if endpoints are unreachable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, and the second clarifies what information is returned. There's zero wasted text, and the information is front-loaded with the most important details first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (status checking with no parameters) and no annotations or output schema, the description provides basic completeness but has significant gaps. It states what the tool does and what it returns at a high level, but doesn't provide enough detail about the return format, authentication requirements, or behavioral characteristics for confident agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema fully documents the empty parameter structure. No additional parameter information is needed or provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does 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 ('Check', 'Returns') and resources ('Modal endpoints', 'endpoint URLs and connection status'). It distinguishes itself from sibling tools by focusing on endpoint status rather than video/image analysis capabilities. However, it doesn't explicitly differentiate from potential similar tools like 'list_capabilities' beyond the specific endpoint focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 doesn't mention prerequisites, appropriate contexts, or contrast with sibling tools like 'list_capabilities' that might provide related information. The agent receives no usage direction beyond the tool's stated purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- 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. While it describes what the tool does conceptually, it lacks important behavioral details: it doesn't specify what the tool returns (output format), whether it processes the entire video or specific frames, performance characteristics, or any limitations. For a video analysis tool with no annotations, this represents significant gaps in behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely well-structured and concise. It starts with a clear purpose statement, then provides a bulleted list of use cases. Every sentence earns its place, there's no redundancy, and the information is front-loaded appropriately. This is an excellent example of efficient documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no annotations and no output schema, the description should provide more complete context for this video analysis tool. While it explains what the tool does conceptually, it doesn't describe what the tool returns, how it processes the video, any limitations or requirements, or how it differs from sibling tools. For a tool with 2 parameters and no structured metadata, the description is insufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters (video_url and comparison_prompt). The description doesn't add any parameter-specific information beyond what's in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as analyzing changes and progression across a video, which is a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'analyze_video' or 'summarize_video', which likely have overlapping functionality. The description provides a clear general purpose but lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Useful for:' section provides clear context about when to use this tool (before/after comparisons, tracking movement, understanding progression, analyzing tutorials). This gives good guidance on appropriate use cases. However, it doesn't explicitly state when NOT to use it or mention alternatives among the sibling tools, which would be needed for a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- 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 states the tool answers questions about video content but does not explain how it works (e.g., AI-based analysis, processing time, limitations like video length or format support), what happens if the question is unclear, or the response format. For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose statement, followed by relevant examples that earn their place by illustrating usage without redundancy. It is appropriately sized—two sentences and a bulleted list—with zero waste, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (Q&A based on video analysis), no annotations, and no output schema, the description is incomplete. It covers the purpose and parameter intent but lacks details on behavioral traits, response format, and limitations. While the examples add context, more information is needed for a tool that performs AI-driven video analysis, making it only adequate with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does 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 ('video_url' and 'question'). The description adds value by emphasizing that questions should be 'specific' and providing examples that illustrate the expected format and scope of the 'question' parameter, though it doesn't detail URL requirements. This goes beyond the schema's basic descriptions, justifying a score above the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Ask a specific question about a video's content.' It specifies the verb ('Ask'), resource ('video's content'), and provides concrete examples that illustrate the scope. This distinguishes it from siblings like 'summarize_video' or 'analyze_video' by focusing on Q&A rather than general analysis or summarization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through examples (e.g., questions about objects, actions, arguments), suggesting it's for specific queries rather than broad analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like 'analyze_video' or 'summarize_video', and does not mention prerequisites or exclusions. The examples help but don't fully clarify the tool's niche among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
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 valuable context about the image accessibility requirement (public URL) and provides example use cases that suggest the tool's capabilities. However, it doesn't disclose important behavioral traits like rate limits, authentication needs, error conditions, or response format expectations that would be crucial for an agent to use 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured and concise. It leads with the core purpose, follows with the critical accessibility constraint, then provides helpful examples that demonstrate the tool's capabilities without being verbose. Every sentence earns its place, and the bulleted examples are efficiently formatted to convey multiple use cases in minimal space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides adequate but incomplete context. It covers the purpose, accessibility requirement, and example use cases well, but lacks information about what the tool returns, error conditions, rate limits, or authentication requirements. For a tool with 3 parameters and no structured behavioral annotations, this leaves significant gaps in understanding how to properly invoke and interpret results from this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It provides example questions that illustrate potential values for the 'question' parameter, but this doesn't add semantic meaning beyond the schema's description. Baseline 3 is appropriate when the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze an image using Qwen2.5-VL vision-language model.' It specifies the exact model being used and distinguishes it from sibling tools like analyze_video, video_qa, and compare_video_frames by focusing specifically on image analysis rather than video processing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does 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: for analyzing images via a specific vision-language model. It explicitly states 'The image must be accessible via a public URL,' establishing a key prerequisite. However, it doesn't explicitly contrast when NOT to use it versus alternatives like analyze_video or video_qa, though the image focus is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes what the tool does (extract and transcribe text/speech) and gives usage examples, but lacks details on behavioral traits like processing time, accuracy, supported video formats, or error handling. This leaves gaps in understanding operational constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with a clear purpose statement followed by a bulleted list of use cases. Every sentence earns its place by providing specific, actionable information without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool that performs complex video processing. It explains the purpose and usage but lacks details on output format (e.g., text structure, timestamps), limitations (e.g., video length, language support), or error conditions, which are important for contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'video_url' clearly documented. The description does not add extra parameter details beyond the schema, but since schema coverage is high and there are no parameters beyond this, a baseline of 4 is appropriate as it adequately covers the minimal parameter set.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb 'extract and transcribe' and the resource 'visible text or speech from a video', making the purpose specific and clear. It distinguishes from siblings like 'summarize_video' or 'analyze_video' by focusing specifically on text extraction rather than analysis or summarization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Useful for' section provides clear context on when to use this tool, listing specific scenarios like reading on-screen text, transcribing speech, and extracting text from presentations. However, it does not explicitly state when not to use it or name alternatives among siblings, such as 'summarize_video' for summaries instead of full text extraction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
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 effectively describes key behaviors: the video must be publicly accessible via URL, the process includes downloading, extracting frames (up to max_frames), analyzing with a question, and providing timestamp-grounded responses. It covers operational steps and constraints, though it lacks details on rate limits, error handling, or specific model capabilities.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with the core purpose, followed by key requirements and a bulleted list of process steps. The examples are relevant and illustrative without redundancy. Every sentence earns its place, making it efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (video analysis with a vision-language model), no annotations, and no output schema, the description does a good job of covering the operational workflow, constraints, and use cases. It explains the process and provides examples, but lacks details on output format (e.g., structure of timestamp-grounded responses) and potential limitations, leaving some gaps for a tool of this nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 (video_url, question, max_frames, max_tokens) with descriptions. The description adds minimal value beyond the schema, such as reinforcing the public URL requirement and hinting at frame extraction limits, but does not provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Analyze a video using Qwen3-VL vision-language model.' It specifies the action ('analyze'), resource ('video'), and method ('using Qwen3-VL vision-language model'), clearly distinguishing it from sibling tools like 'summarize_video' or 'extract_video_text' by emphasizing comprehensive analysis with question-driven responses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does 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 analyzing videos with specific questions, as shown in the examples (e.g., 'What happens in this video?', 'Summarize the main events with timestamps'). It implies usage for detailed, timestamp-grounded analysis but does not explicitly state when not to use it or name alternatives among siblings, though the examples hint at its broad applicability.
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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