Server Quality Checklist

Tool Scores

• segment_journeyA
  3.6/5.0
  Behavior3/5

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

  No annotations are provided, so description carries the burden. It discloses the input formats (CSV or JSON), the segmentation rule, and the output structure (segments with number, start, end, duration, sample count, temperature stats). It doesn't mention edge cases like single-sample segments, how the first/last segment boundary is handled, or data quality behavior for missing timestamps.

  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 reasonably concise with front-loaded purpose, followed by context, parameter docs, and return format. No wasted sentences. The Polish language is consistent throughout, and the structure (purpose, context, args, returns) is logical and scannable.

  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?

  An output schema exists, so return values don't need to be described in detail—yet the description still provides a useful summary of the output fields. No annotations, so the description reasonably covers the tool's behavior. For a 4-parameter tool with CSV/JSON input flexibility, the description is adequate, though it could mention error conditions or how the timestamp field is parsed.

  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 description coverage is 0%, and the description compensates partially by explaining each parameter in Polish: data (CSV/JSON), gap_minutes (the time gap threshold), ts_field and value_field (column names). The names and defaults in the schema are fairly self-explanatory, and the description adds modest context about their roles but doesn't cover defaults meaning or edge cases for malformed data.

  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 verb+resource action: 'Dzieli ciągły log na osobne przejazdy' (divides continuous log into separate journeys) based on time gaps. It distinguishes this from siblings (which focus on stats, threshold detection, and anomaly detection) by specifying the segmentation logic. However, it doesn't explicitly contrast with sibling tools.

  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 explains when to use this tool: when a transport logger records multiple journeys in a single file and you need to split them. It clearly defines the triggering condition (gap longer than gap_minutes marks a boundary). It could be improved by mentioning what it's NOT for (e.g., not for computing summary stats or detecting anomalies, which siblings handle).

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

• summary_statsA
  4/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It explains what return values look like ('JSON ze statystykami') and lists the computed metrics. However, it doesn't disclose edge-case behaviors like handling invalid timestamps, missing values, empty datasets, or whether the computation is exact for large files or approximate.

  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 well-structured with clear sections (purpose, usage guidance, Args, Returns). Every sentence adds value and the information is front-loaded with the core purpose first. No wasteful 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 has an output schema so return value structure is covered elsewhere. The description covers input formats, both optional parameters and their defaults, the purpose, and when to use it. For a relatively simple summarization tool with 3 params and an output schema, the description is reasonably complete, though it could note behavior on edge cases (empty data, non-numeric values).

  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 description coverage is 0%, so the description must compensate. The description explains all three parameters: data accepts CSV or JSON format, ts_field defaults to 'timestamp' for the time column, and value_field defaults to 'value'. However, it doesn't detail the CSV structure beyond 'with header' or provide example formats, leaving some ambiguity for the data parameter.

  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 verb+resource: 'Zwraca statystyki podsumowujące dla szeregu czasowego z czujnika' (returns summary statistics for a time series from a sensor). It explicitly distinguishes itself from manual computation and lists what statistics are returned (count, min, max, mean, median, stddev, time range, duration).

  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 instructs when to use this tool instead of manually computing statistics on raw data ('Użyj tego narzędzia zamiast samodzielnego liczenia statystyk'). It gives clear context but doesn't explicitly compare against sibling tools like detect_anomalies or detect_threshold_breaches, which are related but distinct (those detect patterns, this summarizes).

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

• detect_threshold_breachesA
  4.2/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the behavioral burden. It does a good job explaining the behavioral nuances (duration threshold, hysteresis logic) that govern the tool's detection output. However, it doesn't disclose what happens with missing data, how the JSON vs CSV data detection works, or error cases. The core detection behavior is well disclosed, but edge cases are left undocumented.

  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 well-organized: purpose statement, key behavioral notes, Args list, and Returns section. It front-loads the critical compliance insight before diving into parameters. The Args and Returns sections are clearly structured. Slightly verbose with the compliance preamble, but every sentence adds value for a compliance-critical tool.

  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 has 7 parameters with 0% schema coverage, but the description explains all of them with examples. An output schema exists, so the Returns description doesn't need full detail but still outlines what's returned (list of violations with type, start, end, duration, peak value, and summary). For a complex detection tool with nuanced semantics, the coverage is quite complete.

  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?

  Schema description coverage is 0%, so the description fully compensates. Every parameter is explained: min_temp and max_temp with concrete examples (2.0 and 8.0 for 2-8°C products), None semantics for absent thresholds, min_duration_minutes meaning, hysteresis semantics, and ts_field/value_field purposes. The description adds substantial meaning beyond the bare schema field names.

  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 it detects temperature threshold breaches (cold chain violations). It specifies the exact verb (wykrywa/detects), the resource (threshold breaches), and the domain (cold chain). It provides strong detail about what constitutes a violation (duration, hysteresis), which clearly distinguishes it from sibling tools like detect_anomalies and summary_stats.

  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 explains key compliance semantics: brief momentary spikes often do NOT count as violations, only those sustained past min_duration_minutes count, and hysteresis prevents false repeated alarms. This gives clear context for when to trust the tool's output. However, it doesn't explicitly contrast with sibling tools like detect_anomalies, so there's no direct 'use this instead of X' guidance.

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

• detect_anomaliesA
  4.3/5.0
  Behavior4/5

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

  The Returns section discloses the output shape (JSON with anomaly list and summary). It documents the deterministic nature of the tool ('deterministyczny, powtarzalny wynik'), which is a key behavioral trait. With no annotations provided, the description carries the burden and reasonably discloses the detection methods and return format, though it doesn't specify error conditions or data format constraints.

  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 well-structured with Args and Returns sections, front-loading the purpose and rationale before parameter details. It's compact with no wasted sentences, though the rationale sentence about LLM weaknesses, while useful, could be considered slightly extraneous to purpose.

  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 tool with 6 parameters, low schema coverage, and no annotations, the description provides strong coverage: purpose, both methods, defaults, return format, and the statistical rationale. The output schema exists and is referenced. Minor gaps: exact accepted data formats (CSV/JSON shape) are only mentioned briefly, and error handling isn't discussed.

  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 description coverage is 0%, so the description must compensate. It explains each parameter: data, method (with both options and what each computes), sensitivity (threshold in std deviations, default 3.0), window (with auto behavior at 0), and ts_field/value_field ('nazwy kolumn'). This adds substantial meaning beyond bare schema titles, though exact formats for data strings are only loosely implied.

  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 detects anomalies in time series (outlier points), uses a specific verb+resource structure, and distinguishes it from siblings (e.g., detect_threshold_breaches for a different anomaly type). The rationale for use (LLMs poorly compute statistical deviations on thousands of points) adds real purpose.

  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 explains WHEN to use this tool (when deterministic, repeatable anomaly detection is needed) and contrasts it with the LLM's weakness. It doesn't explicitly name alternatives like detect_threshold_breaches to disambiguate, but sibling tools are clearly different enough. The window auto-sizing note provides practical guidance.

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