Server Quality Checklist

Tool Scores

• tweetsave_batchA
  4.2/5.0
  Behavior4/5

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

  Annotations already indicate read-only, idempotent, non-destructive; description adds batch limit and output format details, consistent with 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?

  Extremely concise: single sentence for purpose, bullet list for use cases, then args/returns/examples; no wasted 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?

  Describes return as 'Array of tweets or a combined feed in markdown format' which is acceptable but could be more precise about tweet object structure; no output schema required.

  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 already covers both parameters with descriptions and enums; description repeats them without adding new meaning; baseline 3 per high schema coverage.

  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?

  Clearly states 'Fetch multiple tweets at once (max 10)' with specific verb and resource, and the max limit distinguishes it from sibling tools like get_tweet.

  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 explicit use cases (collecting from list, building feed, comparing) implying when to use, but does not explicitly state when not to use or list alternatives; sibling names help differentiate.

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

• tweetsave_extract_mediaA
  4.4/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint, idempotentHint, etc. Description adds useful behavioral details like 'Video URLs are the highest quality available' and return structure, enhancing transparency beyond 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?

  Description is well-organized with sections (Args, Returns, Examples), concise, and front-loaded with the main purpose. Every sentence adds value without redundancy.

  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 no output schema, description provides return structure (type, url, dimensions, duration), which covers key aspects. It lacks error handling or limits, but is sufficient for basic use.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100%, so baseline is 3. Description adds examples and clarifies that 'url' can be a URL or ID, and explains the media_type filter with defaults, providing extra semantic value.

  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 clearly states 'Extract direct media URLs' and specifies types (photos, videos, GIFs). It distinguishes from siblings like tweetsave_get_tweet which likely returns tweet text, making the tool's specific resource and action unambiguous.

  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?

  Description provides clear context for when to use this tool (extracting media) and includes examples. However, it does not explicitly state when not to use it or compare with sibling tools, so it lacks exclusions but offers sufficient context.

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

• tweetsave_get_threadA
  4.4/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds that it fetches the main tweet only and full thread requires additional API access, which is useful behavioral context beyond 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?

  Concise: purpose, note, args, returns, examples in a few sentences. Front-loaded and no fluff.

  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?

  No output schema, but description states 'Array of tweets with all content and media.' Provides examples. Sufficient for the tool's complexity.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100% (baseline 3). Description explains url as 'URL or ID of any tweet in the thread' and response_format with 'human-readable or structured data', including an example, adding value beyond enum values.

  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 'Fetch a tweet thread (multiple connected tweets by the same author).' Specific verb+resource, clearly distinguishes from siblings like tweetsave_get_tweet (single tweet) and tweetsave_to_blog (conversion).

  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 a caveat about current implementation only fetching the main tweet and that full thread requires additional API access. This guides when-to-use but does not explicitly name alternatives.

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

• tweetsave_to_blogA
  4.5/5.0
  Behavior4/5

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

  Annotations already indicate readOnly (true), destructive (false), and idempotent (true). The description adds context on what the tool produces (BlogPost with fields) and the transformation process, enhancing understanding.

  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?

  Description is well-structured with sections for purpose, use cases, arguments, return values, and examples. Every sentence adds value, no redundancy.

  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?

  Given 3 parameters, no nested objects, and no output schema, the description covers the tool's functionality thoroughly, including return fields, formats, and use cases. It is complete for effective agent usage.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100% with parameter descriptions. Description repeats and elaborates on parameters, adds examples, and explains the effect of 'include_engagement' and 'response_format', providing additional semantics 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 'Convert a tweet into a formatted blog post', specifying the verb (convert) and distinct resource (tweet to blog post). It differentiates from siblings like get_tweet or extract_media by focusing on transformation and formatting.

  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?

  Description lists specific use cases (archiving, content creation, generating blog posts) and includes example prompts, but does not explicitly contrast with sibling tools or state when not to use.

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

• tweetsave_get_tweetA
  4.7/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds behavioral context by mentioning the use of the FxTwitter API, the output format options (markdown/json), and the return data structure including author info, media URLs, and engagement stats. While it does not discuss rate limits or authentication, the combination of annotations and description provides sufficient 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 structured and front-loaded: first sentence states purpose, followed by paragraphs for arguments, returns, examples, and notes. Every sentence adds value without redundancy. It is appropriately sized for the tool's complexity.

  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 the absence of an output schema, the description provides a good overview of return data (author, text, media, engagement, polls, quote tweets). It also notes limitations. However, it could be more precise about the exact fields and nesting structure, leaving some ambiguity for agents that need strict parsing guidance.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  With 100% schema description coverage, the description adds significant meaning beyond the schema: it explains that `url` can be a tweet URL or ID and provides examples; for `response_format`, it clarifies the two options and their purposes. The 'Returns' section elaborates on the output, compensating for the lack of an output 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 that the tool fetches a single tweet with all its content, including text, media, polls, and engagement metrics. It distinguishes from sibling tools by noting that it does not fetch replies and directs users to `tweetsave_to_blog` for complete blog post formatting.

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

  Usage Guidelines5/5

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

  The description provides explicit guidance on when to use: to retrieve a single tweet. It also specifies what not to use it for (fetching replies) and offers an alternative tool (`tweetsave_to_blog`) for related tasks. Examples of input formats (URL or ID) further clarify usage.

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