Server Quality Checklist

Tool Scores

• platform_limitsA
  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 the burden of behavioral disclosure. It clearly states that the tool lists platforms with their limits, units, explanatory rationale, and sources, and frames it as a read-only informational operation. It does not explicitly mention side effects or authentication, but for a simple listing tool, this is adequate.

  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 two sentences of appropriate length. The first sentence front-loads the action and output contents, while the second sentence adds practical usage guidance. Every clause contributes value, and there is no redundancy or filler.

  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?

  The tool is simple: one optional parameter, no output schema, and modest complexity. The description enumerates the output fields (platform, limit, unit, reason, source) and provides a use case. It could be slightly more explicit about the response format, but it is sufficiently complete for an agent to select and invoke the 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?

  The input schema provides 100% coverage: the only parameter, 'platform', is fully described with an enum of allowed values and a clear description ('Limit the response to one platform. Omit for all of them.'). The description adds no additional parameter-level detail, but none is needed given the schema's completeness.

  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 opens with the specific verb 'List' and clearly identifies the resource: platforms known to the server, along with their limits, units, rationale for non-character-count units, and data sources. This distinguishes it from sibling tools like check_post and check_post_all, which focus on post-length validation rather than enumerating platform limits.

  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?

  The description explicitly states when to use the tool: 'Use it to explain a result or to see what is covered.' This provides clear context. However, it does not explicitly mention when not to use it or alternative tools, so it stops short of a full 5.

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

• check_postA
  4.4/5.0
  Behavior5/5

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

  With no annotations, the description carries the full burden and delivers: it reveals non-obvious counting rules (URLs at fixed width, non-Latin characters double, emoji as one grapheme) and warns that results differ from text.length. This gives the agent valuable insight into the tool's behavior beyond basic function.

  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, no fluff. The most important action is first, followed by return value details and a practical usage tip. Every sentence contributes.

  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?

  The description covers the tool's purpose, return value, counting rules, and usage context. It lacks exact output structure, but with no output schema, the high-level return description is adequate for an agent to understand what to expect. It's complete enough for most scenarios.

  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 already covers 100% of parameters with meaningful descriptions (trimming, placeholder pricing, platform enum). The tool description adds little parameter-specific info beyond the schema, so the baseline of 3 applies.

  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 opens with a specific verb and resource: 'Check a social post against one platform's real character limit and report whether it fits.' It clearly states the core function and differentiates itself from siblings by emphasizing 'one platform' (vs check_post_all) and the idea of limits (vs platform_limits).

  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?

  It gives an explicit usage invitation: 'Use this before publishing anything with a hard limit.' However, it does not explicitly name alternatives or state when not to use it, though the sibling tool names (check_post_all, platform_limits) imply some distinctions.

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

• check_post_allA
  4.5/5.0
  Behavior4/5

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

  With no annotations, the description carries the behavioral burden. It discloses a nontrivial behavior: 'per-platform arithmetic is included only for the platforms it fails, to keep the response small.' It does not cover auth, rate limits, or error cases, but the special response-size behavior is valuable and specific.

  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 tight sentences, front-loaded with the primary purpose, followed by a usage trigger and a behavioral caveat with an alternative. Every sentence earns its place and no filler exists.

  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?

  For a single-parameter tool with no output schema, the description explains the purpose, the output row concept, the compact-response behavior, and the sibling alternative. It is sufficiently complete for an agent to select and invoke 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 coverage is 100% and the text parameter already has rich schema description (trimming, URL placeholders). The tool description itself adds only contextual meaning ('counted length, limit') rather than new parameter-level details, so the baseline 3 is appropriate.

  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 opens with a specific verb+resource: 'Check one post against every platform at once' and clearly states the output shape ('a row per platform: counted length, limit, and whether it fits'). It distinguishes itself from the sibling check_post by noting that check_post gives a single platform's full breakdown.

  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?

  It explicitly says when to use this tool: 'when deciding where a draft can go as-is.' It also provides an alternative by directing users to 'call check_post for a single platform's full breakdown,' making the choice between siblings clear.

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