Server Quality Checklist

Tool Scores

• step_envA
  3.5/5.0
  Behavior2/5

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

  No annotations are provided, so the description must convey behavioral traits. It discloses return values but fails to mention side effects (e.g., environment state mutation), safety concerns, or requirements (e.g., prior reset). For a step function, critical behavioral context is missing.

  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 filler. The first sentence states the core action, the second lists the return values. Information is front-loaded and every sentence adds value.

  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 description covers the return values, but lacks details about the environment's state, action effects, and termination conditions beyond 'agent reached the goal'. Given no output schema, more context (e.g., observation structure) would improve completeness.

  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 0%, so the description must compensate. It adds meaning by specifying '2D acceleration action', suggesting the action array has two numeric components. However, it does not specify the exact dimensions, allowed ranges, or semantics of the acceleration, leaving some ambiguity.

  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 that the tool applies a 2D acceleration action and advances one timestep, and lists return values. This distinguishes it from siblings (reset_env, encode_observation, etc.) by specifying the verb 'apply' and the resource 'environment', making the 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 Guidelines3/5

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

  The description implies usage for stepping the environment, but does not provide explicit guidance on when to use this tool versus alternatives like predict_rollout or when not to use it. No exclusions or prerequisites are mentioned.

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

• reset_envA
  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 bears full burden. It discloses return values (initial observation, agent state, goal position) and mentions deterministic start, but does not explicitly state that the tool destroys the current environment state or any side effects. Some behavioral context is given, but more would improve transparency.

  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 unnecessary words. It front-loads the main action ('Reset the point-mass environment') and concisely details return values. Every sentence is purposeful.

  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 one parameter and no output schema or annotations, the description is nearly complete: it explains the purpose, the role of the seed, and the return values. It could mention that it resets the environment to its initial state, but overall it's adequate.

  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?

  The input schema has 0% coverage, meaning no parameter descriptions. The description adds meaning by stating that the seed controls the deterministic start, clarifying the parameter's role beyond the default value and type in 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 verb 'Reset' and the resource 'point-mass environment', specifying it resets to a deterministic start for a given seed. This distinguishes it from sibling tools like step_env (which advances the environment) and encode_observation (which encodes observations).

  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 this tool (to reset to a deterministic start with a seed) but provides no explicit guidance on when not to use it or alternatives among siblings. The context is clear but lacks exclusions or comparative context.

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

• encode_observationA
  4.1/5.0
  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 use of a trained JEPA encoder and explains the latent space role, but omits side effects, idempotency, or authorization needs.

  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 efficient sentences: first for purpose, second for context. No redundancy, well 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?

  Covers core functionality and output purpose well, but lacks detail on latent vector dimensions or type, which would be helpful given no output schema.

  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?

  The input schema only specifies an array of numbers with 0% description coverage. The description compensates fully by defining the 6D structure and field meanings, adding significant 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 clearly states it encodes a 6D observation into a latent vector, specifying the exact fields [x, y, vx, vy, goal_x, goal_y]. It differentiates itself from siblings by explaining that the latent is used by the world model and downstream 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 description implies usage context (encoding before world model prediction) but does not explicitly state when to use this tool vs alternatives like plan_to_goal or predict_rollout. No when-not or exclusion criteria provided.

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

• plan_to_goalA
  4.4/5.0
  Behavior4/5

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

  The description discloses the algorithmic approach (latent MPC, cross-entropy method) and that it returns the first action of the best plan. No annotations are provided, but the description adequately covers the tool's behavior and expected output.

  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 concise: three sentences covering purpose, method, and usage. Every sentence adds value with no redundancy or 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?

  Given the absence of annotations and output schema, the description sufficiently explains what the tool does and how to use it. It could be slightly improved by mentioning the return format (e.g., action shape), but it is largely complete.

  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?

  The input schema has 0% description coverage, but the description clarifies that current_obs is the current observation and goal_obs is the goal observation. This adds necessary context beyond the raw array type.

  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 purpose: planning the next action toward a goal via latent MPC. It explains the method (encoding observations, searching action sequences) and distinguishes it from sibling tools like encode_observation and predict_rollout.

  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 advises calling the tool repeatedly for receding-horizon control. While it doesn't list when not to use it or compare to alternatives, the replanning guidance is clear and practical.

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

• predict_rolloutA
  4.4/5.0
  Behavior4/5

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

  No annotations provided, so description carries burden. It discloses that it is a simulation using predictor g, no real environment interaction. Lacks details on side effects or error handling.

  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?

  Three sentences, front-loaded with main purpose, efficient and no fluff.

  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 no output schema or annotations, description is fairly complete: explains input, process, and output. Could specify output structure more.

  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 0%, but description explains latent is starting state and actions are list of 2D acceleration actions, adding meaning beyond schema types.

  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 rolls latent dynamics forward from a latent over an action sequence, using specific verbs and resources. It distinguishes from sibling tools (e.g., step_env interacts with environment).

  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 implies usage for simulation without environment interaction, but does not explicitly state when not to use or compare to alternatives like plan_to_goal.

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