Server Quality Checklist

Tool Scores

• logictreeA
  3.6/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full behavioral disclosure burden. It does reveal meaningful traits: remove_node deletes a node and all descendants (destructive behavior), quick_analysis is intentionally focused to avoid overwhelming output, and guidance operations return AI recommendations and exact parameter templates. However, it does not cover persistence, preconditions/permissions, error behavior, or side effects of analyze_tree and generate_hypotheses.

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

  Conciseness2/5

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

  The description is heavily redundant: the Main Features list largely restates the command catalog, the Example AI Session re-walks the Workflow Integration steps, and closing lines like 'This design ensures AI continues using the tool' are self-promotional rather than operational. It is well-structured with headers and emojis, but at roughly 700 words for a simple workflow, many sentences do not earn their place.

  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 is broad for a complex 7-parameter/11-operation tool with no output schema: nearly every operation is described in prose and response shapes are hinted at ('Response includes: current state, AI guidance'). However, move_node is absent from the command catalog despite appearing in the schema enum, and per-operation parameter requirements and return content are only sketched. These gaps matter more precisely because no output schema exists to fill them.

  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 seven parameters and the nested metadata object, placing this at baseline 3. The description adds limited value through concrete payload templates such as add_node with content/nodeType and solution nodes with parentId plus metadata priority/feasibility. These examples illustrate valid combinations but largely duplicate what the schema already conveys.

  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 frames the tool as an AI-guided hierarchical problem analysis system that breaks complex problems into structured logic trees. It enumerates ten named operations grouped into AI Guidance, Basic, and Advanced categories, making its scope explicit. As a multi-operation umbrella tool with no siblings to differentiate from, the stated purpose plus the internal command catalog give an agent a solid model of what the tool does.

  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?

  The 'AI Workflow Integration' section prescribes explicit invocation rules: always start with get_status, run quick_analysis after each major action, and call next_steps when unsure. The 'START HERE' labeling and the ordered workflow give concrete when-to-use conditions for each operation. No sibling tools exist, so external routing is moot, but internal command selection guidance is exceptionally concrete and actionable.

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