reconstruct_tree

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

Discloses that the tree is stored in tdata.obst[key_added] as a rooted DiGraph. No annotations provided, so description carries burden; lacks mention of side effects, idempotency, or required permissions, but covers the essential output behavior.

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

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

Two concise paragraphs with bullet-style method list. Every sentence adds value, no redundancy. Front-loaded with purpose, then structured details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all 6 parameters, methods, and output location. Could mention the DiGraph attributes or return value explicitly, but output schema exists (not shown). Adequate for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description compensates by explaining each parameter's role, defaults, and examples (e.g., method options, extra_options). Adds meaning beyond the schema structure.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool reconstructs a lineage tree from a character matrix, with specific verb 'Reconstruct' and resource 'lineage tree'. Distinguishes from siblings like plot_tree or calculate_parsimony by focusing on tree inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

Provides detailed method descriptions with usage notes (e.g., 'good default', 'exact; requires Gurobi; small trees only') that guide selection. Does not explicitly contrast with sibling tools, but the intent is clear.

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