Server Quality Checklist

Tool Scores

• bittensor_dataB
  3.1/5.0
  Behavior2/5

  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 only discloses cost; it fails to mention return formats (despite no output schema), potential latency from decentralized Bittensor network calls, side effects, or rate limits. The mention of 'subnet 13' hints at the distributed nature but lacks behavioral implications.

  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 consists of exactly two sentences with zero waste: the first establishes function and mechanism, the second states cost. Information is front-loaded with the primary purpose stated immediately, making it highly efficient.

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

  Completeness3/5

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

  Given the single string parameter and 100% schema coverage, the description meets minimum requirements. However, as a synthesis tool with no output schema, it should ideally describe expected return types (structured data, text summary, etc.). The cost disclosure partially compensates for missing annotations, but output documentation remains a gap.

  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?

  The input schema has 100% coverage with 'prompt' described as 'Data analysis request'. The description aligns with this by mentioning 'Data analysis and synthesis' but does not add substantial semantic value regarding prompt format, expected structure, or examples beyond what the schema already provides, warranting the baseline score for complete schema coverage.

  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 clearly states the tool performs 'Data analysis and synthesis' via 'Bittensor subnet 13 (Data Universe)', providing specific verb and resource identification. However, it does not explicitly differentiate from siblings like 'bittensor_scrape' (data collection) or 'bittensor_forecast' (predictive analysis), leaving some ambiguity about when to select this specific subnet tool.

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

  Usage Guidelines2/5

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

  While the description includes cost information ('$0.005 per call'), it provides no explicit guidance on when to use this tool versus alternatives like bittensor_scrape or bittensor_forecast, nor does it mention prerequisites or input requirements beyond the implicit 'analysis' context.

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

• bittensor_codeB
  3.3/5.0
  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 successfully communicates the financial cost per invocation, which is critical for agent decision-making. However, it fails to describe the output format (crucial given no output schema exists), latency expectations, or whether the tool executes generated code versus returning source text.

  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 consists of two efficient sentences with zero waste. The first sentence front-loads the core capability (code generation via specific subnet), and the second sentence provides essential cost information. Every word serves a distinct purpose in aiding tool selection.

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

  Completeness3/5

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

  For a single-parameter tool with complete schema coverage, the description adequately covers the basic purpose and cost. However, given the absence of both annotations and an output schema, the description should have specified the return format (e.g., raw code, JSON wrapper, multiple files) and distinguished this from the general-purpose LLM sibling to be considered complete.

  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?

  The input schema has 100% description coverage for its single 'prompt' parameter, establishing a baseline score of 3. The description does not add any semantic clarifications about the prompt's expected format, examples of valid requests, or constraints beyond the schema's 'Code generation request' definition.

  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 clearly identifies the resource (code) and action (generation) and specifies the unique backend ('Bittensor subnet 11'), which distinguishes it from sibling tools like bittensor_image or bittensor_text. However, it does not explicitly differentiate from bittensor_llm, which could also generate code, and the term 'Advanced' adds vague marketing language without clarifying scope.

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

  Usage Guidelines2/5

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

  The description provides cost information ('$0.01 per call') but offers no guidance on when to use this tool versus similar generation tools like bittensor_llm or bittensor_text. It does not specify supported programming languages, complexity limits, or prerequisites for the prompt parameter.

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

• bittensor_translateB
  3.3/5.0
  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 successfully discloses the financial cost per invocation, but fails to mention other behavioral traits such as latency expectations, content safety policies, or what format of data is returned (critical since no output schema exists).

  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 appropriately concise with two efficient statements: one defining the function and mechanism, the other stating cost. Information is front-loaded with no wasted words or redundant explanations.

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

  Completeness3/5

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

  Given the simple single-parameter input and lack of output schema, the description should ideally specify the return format (translated text). While the cost disclosure adds value, the absence of return value documentation and safety/compliance notes leaves gaps for a tool interfacing with an external decentralized network.

  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?

  The input schema has 100% description coverage for its single parameter, establishing a baseline score of 3. The description does not add additional semantic context, formatting constraints, or examples beyond what the schema already provides for the 'prompt' parameter.

  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 clearly identifies the tool's function (multilingual translation) and specific implementation (Bittensor subnet 3), which helps distinguish it from other bittensor_* siblings. However, it does not explicitly differentiate from bittensor_llm or bittensor_text which might also handle translation tasks.

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

  Usage Guidelines2/5

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

  While the description includes cost information ($0.005 per call), it provides no guidance on when to use this tool versus alternatives like bittensor_llm, or when to avoid it. No prerequisites or contextual triggers are mentioned.

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

• bittensor_multimodalA
  3.5/5.0
  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 adds valuable financial context with the cost disclosure, but lacks other critical behavioral details such as output format (string? JSON?), rate limits, or whether the operation is idempotent/read-only.

  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 consists of two efficient sentences with zero waste. The first sentence front-loads the core capability (multimodal reasoning), and the second provides cost information. Every sentence earns its place.

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

  Completeness3/5

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

  While the cost and function are clear, there is no output schema provided, and the description fails to indicate what the tool returns (e.g., a text analysis, structured data, or confidence scores). For a 2-parameter AI reasoning tool with no annotations, this gap in return value documentation is notable.

  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%, documenting both 'prompt' and 'image_url' parameters fully. The description does not add parameter-specific semantics beyond what the schema provides, meeting the baseline expectation for high-coverage schemas.

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

  Purpose5/5

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

  The description explicitly states 'Image + text reasoning' (specific verb + resource combination) and identifies the specific backend 'Bittensor subnet 24 (Omega Multimodal)'. This clearly distinguishes it from siblings like bittensor_image (likely unimodal) and bittensor_text by emphasizing the multimodal capability.

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

  Usage Guidelines2/5

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

  The description mentions cost ('$0.02 per call') but provides no guidance on when to use this tool versus siblings like bittensor_image or bittensor_llm. There is no mention of prerequisites, required formats, or decision criteria for selecting this over alternatives.

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

• bittensor_reasoningA
  3.5/5.0
  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 successfully discloses the financial cost per invocation and identifies the specific backend mechanism (subnet 4/Targon), but omits information about return format, rate limits, idempotency, or authentication requirements.

  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?

  Three sentences efficiently convey the tool's function, optimal use case, and cost structure without redundancy. Information is front-loaded with the core capability, followed by usage context and operational cost.

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

  Completeness3/5

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

  Given the simple single-parameter input and lack of output schema, the description adequately covers the primary operational context (purpose, cost, and subnet identity). However, it fails to describe the expected output format or structure, which would be helpful given the absence of an output schema.

  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?

  The input schema has 100% description coverage for its single 'prompt' parameter. The description does not add additional semantic details about the parameter beyond what the schema provides, meriting the baseline score for high-coverage schemas.

  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 clearly states the tool performs 'Advanced reasoning' using 'Bittensor subnet 4 (Targon)' and distinguishes its intended use case as 'complex multi-step problems,' differentiating it from siblings like bittensor_text or bittensor_llm. However, it stops short of explicitly naming alternative tools for simpler tasks.

  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?

  Provides implied usage guidance by stating it is 'Best for complex multi-step problems' and includes critical cost information ('$0.05 per call'), but lacks explicit when-not-to-use guidance or named alternatives from the sibling tool set.

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

• bittensor_textA
  3.5/5.0
  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. It successfully discloses cost ('$0.005 per call') which is critical behavioral information, but omits other important traits like idempotency, whether conversations are stateful, rate limits, or output format details.

  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?

  Three sentences efficiently structured: definition, use cases, and cost. Every sentence provides distinct value (what it is, when to use it, financial impact) with no redundancy or fluff.

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

  Completeness3/5

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

  For a single-parameter tool, the description covers the essential purpose and cost. However, given the lack of output schema and annotations, it could be improved by mentioning the return type (generated text), prompt length constraints, or latency expectations. Adequate but has clear gaps.

  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?

  The schema has 100% description coverage ('Your text prompt') and only one parameter. The description adds context about what the prompt is used for (conversational AI) but does not add constraints, format specifications, or examples beyond what the schema provides. Baseline 3 is appropriate for high schema coverage.

  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 clearly identifies the tool as 'Conversational AI via Bittensor subnet 1 (Text Prompting)' with specific use cases (general questions, summaries, chat). It distinguishes itself from specialized siblings like bittensor_image or bittensor_3d by emphasizing 'general' text tasks, though it doesn't explicitly clarify when to use this versus bittensor_llm or bittensor_reasoning.

  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?

  It provides clear use cases ('Good for general questions, summaries, and chat') which implies when to use it, but lacks explicit guidance on when NOT to use it or which sibling tools to use for specialized tasks like coding, image generation, or reasoning.

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

• bittensor_forecastA
  3.6/5.0
  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. It discloses cost and the Bittensor subnet mechanism, but omits other behavioral traits like output format, latency expectations, rate limits, or whether calls are idempotent. The cost disclosure adds value beyond what annotations would provide.

  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?

  Two sentences efficiently structured: first states purpose and mechanism, second states cost. Every sentence earns its place with no redundancy. Information is front-loaded with the core function stated immediately.

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

  Completeness4/5

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

  For a single-parameter tool with no output schema, the description adequately covers the essential context: what it does, what domain it serves, and cost implications. While output format details would be helpful given the lack of output schema, the description is sufficiently complete for tool selection.

  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% with the 'prompt' parameter fully documented in the schema itself ('Forecasting request, e.g...'). The description adds no parameter-specific semantics, but this is acceptable given the high schema coverage, meeting the baseline score of 3.

  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 clearly states the tool performs 'Financial and crypto time series forecasting' using 'Bittensor subnet 8', providing specific verb, resource, and technical mechanism. It implicitly distinguishes from siblings like bittensor_code or bittensor_image by domain specificity, though it doesn't explicitly differentiate from sharpsignal_predict.

  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?

  The description provides cost information ('$0.05 per call') which informs usage decisions, but lacks explicit guidance on when to use this tool versus alternatives like sharpsignal_predict or other bittensor tools. No prerequisites or exclusion criteria are mentioned.

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

• bittensor_ttsA
  3.6/5.0
  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. It successfully discloses output format (base64 MP3) and cost structure, but omits other behavioral traits like potential latency, rate limits, error handling, or side effects of the network call.

  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?

  Extremely efficient three-clause sentence structure. Front-loads the core function (Text-to-speech), follows with output format, and ends with cost. Zero redundant words.

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

  Completeness4/5

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

  Appropriately complete for a simple single-parameter tool. Compensates for the missing output schema by specifying the return format (base64 MP3) and includes cost transparency. Could benefit from brief error handling mention.

  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?

  Input schema has 100% description coverage for the single 'prompt' parameter. The description does not add parameter-specific semantics, meeting the baseline expectation for high-coverage schemas.

  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?

  Clearly states the tool performs text-to-speech using Bittensor subnet 16. Mentioning 'Returns audio as base64 MP3' effectively distinguishes it from sibling tools like bittensor_image, bittensor_video, and bittensor_text which handle different modalities.

  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?

  Provides cost information ($0.025 per call) which aids in usage decisions, but lacks explicit guidance on when to choose this over other bittensor tools or prerequisites for the prompt parameter.

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

• bittensor_scrapeA
  3.7/5.0
  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. It successfully discloses the cost per call, but fails to describe the return format (HTML, text, markdown), error handling for invalid URLs, or rate limiting.

  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?

  Two efficient sentences with zero waste—front-loaded with the purpose and mechanism, followed by critical cost information. No unnecessary verbosity.

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

  Completeness3/5

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

  While adequate for a single-parameter tool, the absence of an output schema means the description should ideally specify the return format (raw HTML vs. extracted text). The cost disclosure partially compensates for missing annotations.

  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 coverage is 100% for the single 'prompt' parameter, which is already well-described in the schema. The description provides context that this is for URLs but doesn't add syntax details or validation rules beyond the schema.

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

  Purpose5/5

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

  The description clearly states the specific action ('Web scraping and URL content extraction') and the mechanism ('via Bittensor subnet 21'), distinguishing it clearly from siblings like bittensor_image or bittensor_code.

  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?

  The description provides cost information ('$0.01 per call') which acts as a usage constraint, but lacks explicit guidance on when to use this versus alternatives or prerequisites like valid URL formats.

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

• bittensor_llmA
  3.8/5.0
  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. It successfully discloses the financial cost and specific network context (subnet 6), but omits other critical behavioral traits like output format, rate limits, or prompt length constraints expected for an inference 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 consists of two high-value sentences with zero waste: the first establishes purpose and specificity, the second establishes cost. It is appropriately front-loaded and sized for a single-parameter tool.

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

  Completeness4/5

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

  For a simple 1-parameter tool without annotations or output schema, the description adequately covers purpose, cost, and model provenance. It could be improved by briefly stating the expected return type (e.g., 'returns generated text') given the absence of an output schema.

  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% (the 'prompt' parameter is fully documented in the schema). The description adds no specific parameter guidance, which is acceptable given the schema completeness, warranting the baseline score of 3.

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

  Purpose5/5

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

  The description provides a specific verb ('inference'), resource ('LLM'), and precise scope ('Fine-tuned', 'Bittensor subnet 6', 'Nous Research') that clearly distinguishes this from sibling tools handling image, video, code, and text modalities.

  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?

  The cost disclosure ('$0.01 per call') provides important usage guidance, but the description lacks explicit guidance on when to choose this tool over sibling text/LLM tools like 'bittensor_text' or 'bittensor_reasoning'.

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

• bittensor_imageA
  4/5.0
  Behavior4/5

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

  No annotations provided, so description carries full burden. It effectively discloses cost ('$0.075 per call') and return format ('Returns an image URL'), which are critical operational details. Does not mention idempotency or error handling, but covers the essential financial and output contract.

  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?

  Three sentences with zero waste: purpose (text-to-image synthesis), output (image URL), and cost ($0.075). Front-loaded with the most important information. No redundant or filler content.

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

  Completeness4/5

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

  For a single-parameter generation tool without output schema, the description adequately covers the return value and operational cost. Missing annotations are compensated by the cost disclosure in text. Sufficient for agent decision-making.

  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 has 100% description coverage ('Image description to generate'), so baseline applies. The description does not add additional parameter semantics (e.g., prompt engineering tips, length limits), but none are required given complete schema documentation.

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

  Purpose5/5

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

  Description provides specific verb ('synthesis'), resource ('image'), and implementation context ('via Bittensor subnet 5'). The modality clearly distinguishes it from siblings like bittensor_text, bittensor_video, and bittensor_code.

  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?

  The modality 'Text-to-image' implies usage context, but there is no explicit guidance on when to select this over siblings (e.g., 'use for static images instead of bittensor_video for motion'). No prerequisites or exclusion criteria mentioned.

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

• bittensor_videoA
  4.2/5.0
  Behavior4/5

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

  With no annotations provided, description carries full burden and comprehensively discloses: async polling behavior, 3-minute timeout, return format (MP4 URL), and per-call cost. Lacks only rate limits or error handling details.

  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?

  Four sentences, zero waste: purpose+subnet, async timing, return format, cost. Front-loaded with primary function, every clause provides essential behavioral or cost information.

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

  Completeness4/5

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

  Given single parameter, no annotations, and no output schema, description adequately covers critical operational aspects (cost, async behavior, timeout, return type). Minor gap in error state documentation.

  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 has 100% description coverage ('Video description to generate'), establishing baseline. Description does not add syntax details, examples, or prompt engineering guidance beyond schema definition.

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

  Purpose5/5

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

  States specific verb ('generation') and resource ('video'), with explicit differentiation from siblings via 'Bittensor subnet 18' (distinguishing from bittensor_image, bittensor_3d, bittensor_tts, etc.).

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

  Usage Guidelines4/5

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

  Provides clear usage context through temporal constraints ('Async — polls until ready (up to 3 min)') and cost disclosure ('$2.00 per call'), guiding when to use vs alternatives, though does not explicitly name sibling alternatives.

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

• sharpsignal_predictA
  4.2/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and discloses critical behavioral traits: monetary cost per invocation, specific three-part output structure (bull case, bear case, implied probability), reliance on live web search, and underlying model (sonar-reasoning-pro). Missing rate limits or error handling details.

  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?

  Four efficient sentences with zero waste: market positioning ('Prediction market intelligence'), core mechanics, attribution, and cost warning. Critical cost information is front-loaded in the final sentence where it won't be missed.

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

  Completeness4/5

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

  Compensates well for the missing output schema by explicitly describing the structured return format (bull/bear/probability) and including cost data. Adequately covers the single parameter's intent. Would be a 5 if it mentioned behavior for ambiguous/non-yes/no inputs.

  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 coverage is 100% with the 'prompt' parameter fully documented in the schema including an example. The description reinforces the yes/no constraint but does not add syntactic or semantic details beyond what the schema already provides, warranting the baseline score.

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

  Purpose5/5

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

  Description uses specific verb+resource ('Submit any yes/no question and get back...') and clearly distinguishes from bittensor siblings by specifying 'Powered by Perplexity sonar-reasoning-pro', indicating this is a third-party prediction service rather than on-chain Bittensor inference.

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

  Usage Guidelines4/5

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

  Provides clear context for when to use (yes/no prediction market questions requiring bull/bear case analysis) and includes cost information ($0.25) that guides selection. Lacks explicit comparison to sibling 'bittensor_forecast', but the Perplexity attribution and cost data provide implicit differentiation.

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

• bittensor_3dA
  4.3/5.0
  Behavior5/5

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

  Excellent disclosure given no annotations: reveals async polling behavior ('polls until ready'), timeout constraint ('up to 3 min'), return format ('GLB file URL'), and cost ('$0.75 per call'). This compensates fully for missing annotations.

  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?

  Five information-dense sentences with zero waste. Structure is logical: purpose → requirements → behavior → return value → cost. Every sentence earns its place.

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

  Completeness5/5

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

  Despite no output schema, description fully compensates by specifying the GLB return format. Covers critical operational details (async nature, 3-minute timeout, cost) that agents need to handle this tool correctly.

  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 has 100% coverage with clear descriptions for both 'prompt' and 'image_url'. Description reinforces the image URL requirement but does not add significant semantic detail beyond what the schema already provides, warranting the baseline score.

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

  Purpose5/5

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

  Description provides specific verb ('generation'), resource ('3D asset'), and mechanism ('via Bittensor subnet 29'). The 'Image-to-3D' scope clearly distinguishes it from siblings like bittensor_image, bittensor_video, and bittensor_text.

  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?

  Provides implied usage through specific scope ('Image-to-3D asset generation') and mentions prerequisite ('Requires a source image URL'), but lacks explicit when-to-use guidance or comparison against sibling tools like bittensor_image.

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