Server Quality Checklist

Tool Scores

• trace_breakdownB
  3.3/5.0
  Behavior3/5

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

  There are no annotations, so the description carries the transparency burden. It discloses that the tool produces step-by-step scores and flags specific issue types (redundant calls, reasoning gaps, goal drift), which gives some behavioral insight. However, it does not describe output format, side effects (likely none), or any limitations, leaving moderate ambiguity.

  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, front-loaded with the core function and then giving concrete examples of what it flags. Every word adds value, with no redundancy or filler. It is appropriately sized for the tool's simple interface (one parameter).

  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?

  The tool has one parameter and no output schema, so the description needs to convey the tool's behavior and return value. It explains what the tool does (scoring and flagging) but does not specify the output structure, scoring scale, or how the results are presented. It is adequate but leaves gaps for an agent trying to use the result.

  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 provides a complete description for the only parameter ('trace') with 100% coverage. The tool description adds no additional meaning about the parameter. Since schema coverage is high, the baseline of 3 applies, and the description does not enhance understanding of the parameter beyond what the schema provides.

  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: 'Step-by-step scoring of every agent decision' with a specific verb ('scoring') and resource ('agent decision'). It goes further to list concrete issues it flags (redundant tool calls, reasoning gaps, goal drift), which distinguishes it from the sibling tools 'judge_trace' and 'efficiency_score' despite not naming them explicitly.

  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 explicit guidance is given on when to use this tool versus alternatives. The description implies it is for detailed per-decision analysis, but it does not state exclusions or mention sibling tools. An agent would not know whether to choose this over 'judge_trace' or 'efficiency_score' based on the description alone.

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

• judge_traceB
  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 carries the behavioral burden. It discloses main outputs (verdict, grade, explanation) and that it scores across four dimensions, but does not mention error handling, side effects, or what the four dimensions are. This is adequate but not rich 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 a single, front-loaded sentence that wastes no words, efficiently covering purpose, method, and outputs. It is concise and well-structured.

  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 2-parameter tool without an output schema, the description covers the essential aspects: what it does, what it returns, and that it evaluates four dimensions. It omits the specific dimensions and lacks usage guidance, but the core information for invoking the tool is present.

  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 both parameters described ('Agent trace as a JSON string' and 'Optional: override the goal stated in the trace'). The description adds no parameter-specific detail beyond the schema, so baseline 3 is appropriate.

  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's function: 'Diagnoses an agent trace' and specifies outputs (verdict, grade, plain-English explanation) and that it 'scores performance across four dimensions.' However, it does not explicitly differentiate from sibling tools like trace_breakdown or efficiency_score, so it is clear but not fully distinguishing.

  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. It only explains what the tool does, not the contexts that favor it over trace_breakdown or efficiency_score, nor any exclusions or prerequisites.

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

• efficiency_scoreA
  3.9/5.0
  Behavior3/5

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

  With no annotations, the description carries the burden of disclosing behavioral traits. It explains that the analysis is deterministic, requires no API key, and runs instantly, which is useful. However, it does not disclose whether the operation is read-only, potential side effects, or what happens on invalid input, leaving some ambiguity.

  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 sentence that front-loads the purpose and immediately adds key behavioral cues (deterministic, no API key, instant). Every word contributes value with no redundancy or irrelevant detail.

  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?

  The tool is simple with one parameter and no output schema. The description explains the purpose and key benefits but does not clarify the return format or structure beyond calling it an 'efficiency analysis'. Given the absence of an output schema, this is a notable gap, though the name 'efficiency_score' partly compensates.

  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 for the single parameter 'trace', describing it as a JSON string conforming to the AgentTrace schema. The description adds no parameter-specific meaning beyond the schema, so it relies entirely on the schema's documentation. The baseline of 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 clearly identifies a specific purpose: deterministic efficiency analysis of token usage, tool redundancy, and latency. It distinguishes itself from siblings by focusing on efficiency metrics rather than judgment (judge_trace) or decomposition (trace_breakdown), making it easy for an agent to select when such analysis is needed.

  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 provides clear context for usage: it is deterministic, requires no API key, and runs instantly. These traits suggest appropriate scenarios (e.g., quick, low-cost analysis). However, it does not explicitly state when not to use it or mention alternative tools, so it falls short of the highest level.

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