Server Quality Checklist

Tool Scores

• calibrate_thresholdB
  3.4/5.0
  Behavior3/5

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

  With no annotations provided, the description must carry the transparency burden. It discloses that the tool sweeps a range and reports a tradeoff, implying read-only analysis, but it does not explicitly state side effects, return format, or whether any state is mutated. This is a moderate amount of behavioral disclosure.

  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 with no fluff. Every sentence adds value: the first states the function, the second gives usage guidance. It is efficiently front-loaded.

  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?

  The complexity is high: 5 parameters, a nested 'fields' object, no output schema, and low schema coverage. The description does not explain parameter semantics, return structure, or how 'high_stakes' affects behavior. It is insufficient for an agent to fully understand the tool's inputs and outputs.

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

  Parameters2/5

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

  Schema description coverage is only 20% (only 'high_stakes' has a description). The tool description does not compensate by explaining parameters like 'fields', 'low', 'high', or 'step'. It only alludes to a range conceptually, which is insufficient for agents to correctly construct inputs.

  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 verb 'Sweep' clearly indicates the action, and 'confidence threshold across a range' specifies the resource. 'Report how review load trades against errors' defines the output. However, it does not explicitly distinguish this from the sibling tool 'evaluate_fields', so it 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 Guidelines4/5

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

  The description states 'Use this to choose a bar deliberately rather than accepting a default', which gives a clear when-to-use context. It does not mention when not to use it or any alternative tools, so exclusions are missing.

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

• evaluate_fieldsA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden. It discloses the return type (disposition per field) and the exact possible values ('post, review, or escaped'), and signals that it is a decision/routing tool rather than a mutating one. This is transparent enough for safe use, though it doesn't elaborate on side effects (likely none) or edge cases.

  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, front-loaded with the core action and result. The second sentence gives a clear call-to-action. No wasted words or repetition of schema 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?

  The description covers the essential context: what it decides, what it returns (including the three disposition values), and when to call it. It does not fully elaborate on the decision logic (e.g., how thresholds are applied), but that is partially covered by the schema. For a tool with a fairly rich parameter set, this is adequately 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 already provides informative descriptions for all parameters, including the meaning of confidence, verification, and the threshold bars. The description itself does not add much parameter-level detail beyond naming the dispositions, so it meets the baseline for high schema coverage but does not exceed it.

  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 uses a specific verb ('Decide') and names the resource ('extracted fields') and the outcome (disposition per field: post, review, or escaped). It clearly distinguishes from its sibling by framing this as the pre-action evaluation step ('Call this before acting on extracted data').

  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 explicitly states when to use the tool ('before acting on extracted data'), which is clear contextual guidance. However, it does not mention alternatives or exclusions relative to the sibling tool 'calibrate_threshold', so it stops short of full comparative guidance.

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