Server Quality Checklist

Tool Scores

• compare_runsA
  3.6/5.0
  Behavior3/5

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

  With no annotations, the description carries full burden. It explains the per-case comparison and that it uses the two most recent runs, but omits edge cases like missing runs or output behavior.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

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

  The description is a single, clear sentence. It is concise but could be more structured with separate statements for purpose and behavior.

  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 no output schema, and the description does not explain return values or how results are presented. While the purpose is clear, completeness is lacking for a comparison 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 coverage is 100% and the description complements the parameter by indicating its role in identifying the suite. But it adds no new details 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 the tool compares the two most recent eval runs per-case to detect regression, with a specific verb and resource. It distinguishes itself from siblings like search or grade_answer.

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

  Usage Guidelines3/5

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

  The description implies when to use (checking regression) but does not provide explicit guidance on when not to use or mention alternatives. It relies on context of sibling names.

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

• searchA
  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 full burden. It discloses the algorithm (BM25), corpus nature (small bundled), and output (top matches with scores). However, it does not address permissions, query limits, or whether the search is destructive. It is adequate but not thorough.

  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 conveys the essential information (algorithm, corpus, output). No wasted words.

  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 simple search tool with 2 parameters and no output schema, the description fully covers the functionality: what it searches over, which algorithm, and what it returns. No gaps given the tool's complexity.

  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 covers both parameters with descriptions (100% coverage). The description adds no extra meaning beyond the schema, so baseline score of 3 is appropriate. The schema already defines 'query' as 'search terms' and 'k' with default and description.

  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 specifies the tool's function as 'BM25 keyword search over a small bundled document corpus' and states the output ('returns the top matches with their scores'). The specific verb 'search' and resource 'small bundled document corpus' distinguishes it from sibling tools like 'calc' or 'model_drift'.

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

  Usage Guidelines3/5

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

  The description explains what the tool does but provides no explicit guidance on when to use it versus alternatives, such as when the corpus is appropriate or limitations. Sibling tools are different, so implicit differentiation exists, but no when-not or alternative recommendations are given.

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

• calcA
  4.2/5.0
  Behavior4/5

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

  Discloses safety constraints (no code execution, rejection of names/calls/imports) which is critical. Lacks details on output format, but sufficient for a simple calculator.

  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?

  Single sentence with essential information and safety note. No extraneous 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?

  Complete for a simple tool with one parameter. Lacks specification of return type or error handling, but adequate given no output schema.

  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 a descriptive example. Description adds safety context but does not further clarify the parameter beyond what schema provides.

  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 it evaluates arithmetic expressions safely, with explicit rejection of code execution. Distinguishes from siblings like search or grade_answer.

  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?

  Indicates safe usage for arithmetic, but does not explicitly specify when not to use or provide alternatives. Context signals show no obvious overlap with siblings.

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

• grade_answerA
  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 full burden. It discloses that the tool is 'Deterministic and lexical, not a model judgement', which is a key behavioral trait. It also explains what it reports (unsupported sentences). However, it does not mention any side effects, rate limits, or output format details.

  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 consists of two sentences with no wasted words. The first sentence states the core action and result; the second sentence provides usage guidance and a behavioral note. Every sentence earns its place.

  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 3-parameter set with full schema coverage and no output schema or annotations, the description covers purpose, usage, and a key behavioral trait (deterministic). However, it lacks information about the return value format (e.g., does it return a list of unsupported sentences? a score?), which would help an agent interpret results 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 schema coverage is 100% (all 3 parameters have descriptions). The tool description adds marginal value beyond the schema: it characterizes the unsupported sentences as 'fabricated figures and claims the sources never make', but this is more about purpose than parameter semantics. The threshold parameter's description in the schema is already clear; the tool description doesn't add new meaning.

  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 the tool's function: 'Check a draft answer against its sources and report which sentences the sources do NOT support'. It uses specific verbs ('check', 'report') and a specific resource ('draft answer', 'sources'). The sibling tools (calc, search, model_drift, compare_runs) are unrelated, so there's no ambiguity.

  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 says when to use the tool: 'Call this on your own answer before giving it to the user when the answer is supposed to be grounded in provided material.' This provides clear context, though it does not explicitly state when not to use it or mention alternatives.

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

• model_driftA
  4.8/5.0
  Behavior4/5

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

  No annotations provided, so description carries full burden. It discloses that the data is from a 'public, frozen eval suite' and that metrics include accuracy, latency, answer length, reliability, refusal rate. It also mentions weekly runs and drift tracking. While rate limits or auth are not mentioned, the read-only nature is clear from 'look up'. A minor gap is absence of explicit read-only declaration, but context sufficient.

  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 well-structured sentences. First sentence states the action and key metrics; second sentence provides use case and optional parameter usage. No fluff.

  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 no output schema, the description enumerates the returned metrics (accuracy, latency, answer length, reliability, refusal rate) and mentions the comparison to previous weekly run. For a simple lookup tool with one optional parameter, this is complete enough for an agent to understand input and output.

  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?

  Only one parameter 'model' with schema description. The description adds concrete examples ('gpt-5', 'claude-opus', 'gemini') and the behavior when omitted ('list every tracked model'). Schema coverage is 100%, yet description still adds value 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 uses a specific verb phrase 'Look up how a live LLM is currently scoring on a public, frozen eval suite' and clearly identifies the resource (model performance metrics). It distinguishes from sibling tools like calc or search by focusing on drift detection.

  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?

  Explicitly states when to use: 'Use it to check whether a model you're about to rely on has quietly changed.' Provides an alternative usage: 'Omit model to list every tracked model.' No ambiguity.

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