Server Quality Checklist

Tool Scores

• inject_observationA
  4/5.0
  Behavior3/5

  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 async nature and that the step count is not advanced, which is useful. However, it does not mention the potentially state-mutating state_patch behavior or any side effects beyond delivering the observation, leaving part of the behavioral profile to the schema.

  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 with no wasted words: the primary purpose is front-loaded, followed by a high-value behavioral caveat. Every clause 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?

  For a moderately simple injection tool, the combination of description and schema covers the required parameters, the optional state_patch semantics, and the key behavioral distinction (no step count advance). Missing details like return values and error behavior are not critical for invoking the tool correctly, and no output schema exists to contradict this.

  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 and their meanings. The tool description adds little beyond examples already present in the observation parameter's schema description, so baseline 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?

  States a specific action ('Deliver an asynchronous environment observation / event alert'), a target ('active session'), and concrete examples. The caveat 'Does not advance the step count' distinguishes it from the step-advancing sibling execute_step, making the tool's role unambiguous.

  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?

  Provides clear context for when to use the tool: asynchronous environment notifications, background alerts, customer orders, and external world drift. It implies a contrast with step-advancing tools via the 'does not advance the step count' statement, though it does not explicitly name alternative tools or list when-not-to-use conditions.

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

• parse_turn_responseA
  4/5.0
  Behavior4/5

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

  With no annotations, the description carries the behavioral burden and discloses key traits: the reasoning trace R_t is discarded, the structured payload is extracted, and JSON syntax slips are mitigated. It stops short of specifying failure behavior when a fenced JSON block is absent, but for a pure parse operation this is meaningful 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?

  Two dense sentences with no filler: one states the input format and payload contract, the other states the output behavior and tolerance for JSON slips. The main verb and resource are front-loaded.

  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 single-parameter pure function with no annotations and no output schema, the description provides the expected input format, the extraction logic, and the structured payload. Error-handling details and an explicit return shape are the only notable omissions, but the given specification is sufficient for an agent to call it 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?

  Schema coverage is 100%, so the baseline applies; the schema already describes response_text as the raw LLM response. The description reinforces the expected fenced-JSON format and the state_patch/action shape in prose, but does not add parameter-level detail 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 names a specific action (parses), a specific resource (raw LLM response string with a fenced JSON block), and the expected payload shape ({'state_patch': {...}, 'action': '...'}). It also clarifies that reasoning is separated for discarding, which makes the tool's role distinct from the execution-oriented 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 Guidelines3/5

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

  The intended use is implied: use this tool when you have a raw LLM response containing free-form reasoning and a fenced JSON block. However, it does not explicitly state when not to use it or mention alternative tools, so the trigger condition is left to inference.

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

• close_sessionA
  4.5/5.0
  Behavior5/5

  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, and it does so well: it states that removal is permanent, that execution state is deleted, and that a final snapshot is returned for auditability. This gives the agent the key safety and side-effect information before invocation.

  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 action and consequence before mentioning the return value. Every phrase earns its place without 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?

  For a simple one-parameter destructive tool with no annotations or output schema, the description covers the essential context: what is removed, that it is permanent, and what the caller receives. Nothing critical is missing for correct invocation.

  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% and the session_id parameter already documents itself as the identifier of the session to remove. The description adds no additional parameter-level semantics, so 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 uses a specific verb-resource pair, 'permanently remove a skill session and its execution state,' and labels itself as housekeeping, making the teardown purpose unmistakable. It also distinguishes itself from siblings like initialize_skill and execute_step by focusing on session termination rather than setup or execution.

  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 'Housekeeping' framing clearly signals that this tool is for cleanup and session removal, which gives an agent the context to choose it when a session is no longer needed. It does not explicitly name exclusions or alternatives, but the lifecycle role is clear enough given the sibling set.

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

• initialize_skillA
  4.5/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the behavioral disclosure burden. It discloses the stateful side effect of creating a session, storage of immutable P and initial state Σ_0, the returned initial payload, and the lack of message history. This goes beyond the schema and materially changes how an agent should treat the tool's effects.

  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 filler. The first sentence establishes the action and result, and the second provides the critical behavioral constraint that the agent must follow. Every sentence earns its place and the most important information is front-loaded.

  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 there is no output schema and no annotations, the description covers the essential operational facts: what is created, what is stored, what is returned, and how the session should be advanced. The rich input schema handles parameter-level details, while the description supplies the workflow context an agent needs to invoke this tool correctly.

  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 coverage is 100%, so the baseline is 3, but the description adds meaningful semantic context by linking skill_specification to 'immutable skill specification P' and initial_state to Σ_0. It also emphasizes that this payload becomes the entire conversational context, which clarifies the significance of those parameters beyond their schema descriptions.

  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 opens with a specific verb and resource: 'Create a new SKILL.state execution session.' It clearly explains the tool's role as the session initializer, distinct from subsequent advancement. It also names what the tool returns and what it stores, making its purpose unambiguous.

  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 usage context by stating that after initialization the payload is the LLM's ENTIRE context and 'Advances happen exclusively through execute_step.' This explicitly routes subsequent behavior to a sibling tool. It does not discuss inject_observation, parse_turn_response, or close_session, but for initialization the key alternative is covered.

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

• execute_stepA
  4.7/5.0
  Behavior5/5

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

  With no annotations, the description carries the full behavioral burden, and it delivers: it discloses that reasoning_trace is discarded and never stored, logged, hashed, or replayed, that old observations are not retained, and that state updates use null-deletion merge semantics. These are precisely the non-obvious runtime behaviors an agent needs to know.

  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 densely informative yet well structured with INPUT, LOGIC, and OUTPUT sections, and the first sentence states the core purpose immediately. Every sentence contributes either behavioral disclosure, parameter semantics, or execution order, with no filler.

  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?

  This is a complex 5-parameter tool with no output schema, but the description fully specifies the input contract, the execution pipeline, and the exact next-turn return tuple. It also covers retention and discard behavior. Minor failure-mode details are absent, but nothing needed for correct selection and invocation is missing.

  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 coverage is 100%, so the baseline is 3, but the description adds real semantic value beyond the schema. It explains that state_update is a sparse patch with null-deletion semantics, that reasoning_trace is a scratchpad that is discarded, and that environment_observation is the previous response's latest_observation used for context-drift detection.

  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 opens with a precise verb-resource pair: 'Advance a skill session by exactly one step t → t+1.' It clearly defines this as the per-step execution tool and distinguishes it from the sibling session lifecycle tools by describing the strict next-turn output tuple.

  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 gives clear operational context: it explicitly says this advances a session by exactly one step and consumes the current observation to produce the next one. It does not explicitly name sibling tools or state when not to use it, but the t → t+1 contract makes the intended usage unambiguous.

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