Server Quality Checklist

Tool Scores

• create_postC
  2.9/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden but only states the basic function. It doesn't disclose critical behavioral traits like authentication requirements, rate limits, whether posts are permanent or draftable, error handling, or what happens when posting to multiple platforms. The mention of 'return a preview' via the postImmediately parameter is helpful but insufficient.

  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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every element ('create and post', 'social media platforms', 'natural language instructions') contributes directly to understanding the tool's function.

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

  Completeness2/5

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

  For a complex tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (e.g., post IDs, success status), error conditions, or how it handles the various parameters like conversationId or actionableInsights. The sibling tools suggest a broader context that isn't addressed.

  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%, so parameters are well-documented in the schema itself. The description adds minimal value beyond the schema by implying natural language processing for the 'instruction' parameter, but doesn't explain parameter interactions or provide additional context for the 8 parameters.

  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 action ('Create and post content') and target ('social media platforms'), with the specific method 'based on natural language instructions' distinguishing it from typical API-based posting tools. However, it doesn't differentiate from sibling tools like 'get_trending_topics' or 'research_topic', which serve completely different purposes.

  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?

  No guidance is provided about when to use this tool versus alternatives or prerequisites. The description mentions posting to social media but doesn't specify use cases, constraints, or how it relates to the sibling tools for research and trend analysis.

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

• get_trending_topicsC
  2.9/5.0
  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 states what the tool does but doesn't reveal any behavioral traits such as rate limits, authentication requirements, data freshness, or whether it's a read-only operation. For a tool fetching live social media data, 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.

  Conciseness5/5

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

  The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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

  Completeness2/5

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

  Given the complexity of fetching trending topics from social media (which involves real-time data, platform-specific behaviors, and potential rate limits), the description is insufficient. With no annotations, no output schema, and minimal behavioral context, it doesn't provide enough information for an agent to understand the tool's full scope and limitations.

  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 description coverage is 100%, with all parameters documented in the schema itself. The description doesn't add any meaning beyond what the schema provides, such as explaining how 'all' works for the platform parameter or what categories are available. Baseline 3 is appropriate when the schema does the heavy lifting.

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

  Purpose4/5

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

  The description clearly states the verb 'Get' and the resource 'trending topics from social media platforms', making the purpose unambiguous. It doesn't explicitly distinguish from sibling tools like 'create_post' or 'research_topic', but the action is specific enough to imply a read-only operation distinct from creation or research.

  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 no guidance on when to use this tool versus alternatives like 'research_topic' or other potential tools for social media analysis. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone.

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

• research_topicC
  2.9/5.0
  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 but offers minimal information. It mentions the search engines used but doesn't describe rate limits, authentication needs, response format, pagination, or what constitutes 'research' output. For a tool with 5 parameters and no annotations, this is insufficient behavioral context.

  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 extremely concise at just 6 words, with zero wasted language. It's front-loaded with the core action and immediately specifies the search engines used. Every word serves a purpose in this minimal but complete statement of function.

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

  Completeness2/5

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

  For a research tool with 5 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the research output looks like, how results are structured, what sources are consulted beyond the named search engines, or limitations of the research process. The agent would have significant gaps in understanding how to effectively use this tool.

  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%, so all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete, but doesn't provide additional contextual meaning about how parameters interact.

  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 verb ('research') and resource ('topic') with specific methods ('using Brave Search and Perplexity'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_trending_topics' or 'create_post', which would require more specific scope definition.

  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 no guidance on when to use this tool versus alternatives like 'get_trending_topics' for trending information or 'create_post' for content creation. There's no mention of prerequisites, limitations, or appropriate contexts for choosing this research tool over other methods.

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