Server Quality Checklist

Tool Scores

• rtm_create_narrative_treeC
  2.9/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Create' implying a write operation, but doesn't specify if this is idempotent, requires specific permissions, or has side effects like storing data. It also omits details on output format, error handling, or performance characteristics.

  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, clear sentence with zero waste. It's front-loaded and efficiently conveys the core purpose without unnecessary elaboration, making it easy to parse quickly.

  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?

  Given the complexity of creating an encoding model with 5 parameters and no output schema, the description is insufficient. It doesn't explain what a 'Random Tree Model encoding' entails, the format of the output, or how the parameters influence the result. This leaves significant gaps for an AI agent to understand the tool's behavior.

  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 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond the schema, such as explaining how 'maxBranchingFactor' and 'maxRecallDepth' affect the encoding quality or performance. Baseline 3 is appropriate when the schema handles parameter documentation.

  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 action ('Create') and the resource ('Random Tree Model encoding of a narrative text'), making the purpose evident. However, it doesn't differentiate this tool from its siblings (rtm_find_optimal_depth, rtm_generate_ensemble, rtm_traverse_narrative), which likely operate on similar narrative data but with different functions.

  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 its siblings or alternatives. It lacks context about prerequisites, such as whether the text needs preprocessing, or when this encoding method is preferred over other narrative analysis tools.

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

• rtm_find_optimal_depthC
  2.9/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'optimal traversal depth' and 'target recall length', implying a computational or analytical operation, but fails to describe key behaviors such as what 'optimal' means (e.g., based on efficiency, accuracy), whether it's a read-only or mutative process, performance characteristics, or error handling. This leaves significant gaps for an agent to understand how the tool behaves beyond its 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?

  The description is a single, clear sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded and efficiently communicates the core function, making it easy for an agent to parse quickly.

  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?

  Given the complexity implied by terms like 'optimal traversal depth' and 'recall length', and with no annotations or output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a depth value, a report), how 'optimal' is determined, or the computational context. For a tool with 5 parameters and analytical nature, more detail is needed to guide an agent effectively.

  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 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what the schema provides (e.g., it doesn't explain how parameters like 'maxBranchingFactor' or 'maxRecallDepth' relate to finding the optimal depth). Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate with extra insights.

  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 action ('Find') and the goal ('optimal traversal depth to achieve a target recall length'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'rtm_traverse_narrative' or 'rtm_create_narrative_tree', leaving some ambiguity about when to use this versus those alternatives.

  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 its siblings (e.g., 'rtm_traverse_narrative', 'rtm_create_narrative_tree', 'rtm_generate_ensemble'). It lacks context about prerequisites, alternatives, or exclusions, leaving the agent to infer usage based on the purpose alone.

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

• rtm_generate_ensembleC
  2.9/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions generating an ensemble for modeling recall, but fails to describe key behaviors such as computational requirements, output format, whether it's a read-only or mutating operation, or any side effects like resource usage. This leaves significant gaps in understanding how the tool behaves beyond its 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?

  The description is a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action and goal, making it easy to parse and understand quickly, which is ideal for conciseness.

  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?

  Given the complexity of generating a statistical ensemble with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't cover behavioral aspects, usage context, or what the output entails, leaving the agent with incomplete information for effective tool invocation in this data-rich environment.

  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 100%, so the input schema already documents all parameters thoroughly. The description adds no additional semantic context about parameters beyond what's in the schema, such as explaining relationships between parameters or typical use cases. Thus, it meets the baseline but doesn't enhance parameter understanding.

  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 action ('Generate a statistical ensemble of Random Trees') and the purpose ('to model population-level recall'), which is specific and informative. However, it doesn't explicitly differentiate from sibling tools like 'rtm_create_narrative_tree' or 'rtm_find_optimal_depth', which likely involve similar tree-based operations, so it doesn't reach the highest score.

  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. With sibling tools such as 'rtm_create_narrative_tree' and 'rtm_find_optimal_depth' available, there's no indication of the specific context or scenarios where generating an ensemble is preferred over other tree-related operations, leaving the agent without usage direction.

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

• rtm_traverse_narrativeC
  2.9/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'traverse' and 'get summaries' but lacks critical details: whether this is a read-only operation, if it modifies data, what the output format looks like, or any performance/rate limits. For a tool with 5 parameters and no annotations, this is insufficient.

  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 front-loads the core action ('traverse a narrative tree') and outcome ('get summaries at varying abstraction levels'). Every word earns its place with zero redundancy or wasted phrasing.

  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?

  Given the tool's complexity (5 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain what a 'narrative tree' is, how summaries are generated, the format of results, or error conditions. For a tool that likely produces structured output, this leaves significant gaps for an AI agent.

  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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by hinting at 'different depths' and 'varying abstraction levels', which loosely relates to 'traversalDepth', but doesn't provide additional semantic context beyond what's in the schema. This meets the baseline for high schema coverage.

  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 purpose with a specific verb ('traverse') and resource ('narrative tree'), and indicates the outcome ('get summaries at varying abstraction levels'). However, it doesn't explicitly differentiate from sibling tools like 'rtm_create_narrative_tree' or 'rtm_find_optimal_depth', which prevents a perfect score.

  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 its siblings (rtm_create_narrative_tree, rtm_find_optimal_depth, rtm_generate_ensemble). It doesn't mention prerequisites, alternatives, or specific contexts for application, leaving the agent without clear usage direction.

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