Zovo Decision Tree

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

Goes well past the readOnly/idempotent annotations by disclosing exactly which defects are reported, that a clean tree additionally yields the root expected value, and that it refuses unreadable trees (unknown parent, loop, two roots, over limits) with the offending line or id named. That refusal/error behavior is the kind of thing annotations cannot express.

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?

Front-loaded with the when-to-use trigger and examples, then the return payload, then refusals, then exclusions — a sensible order. The middle sentence is a long semicolon-delimited enumeration of checks that is dense but each item is a distinct diagnostic, so little is truly filler.

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?

For a validating tool with 3 optional params, the description covers triggers, accepted inputs, every reported defect category, the clean-case extra output, and hard failure modes. Even though an output schema exists, the enumeration of diagnostics makes the result shape predictable before calling.

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?

Schema description coverage is 100%, including a full outline grammar and worked example, so the description's input discussion is largely a pointer to parity with make_decision_tree rather than new semantics. The only added meaning is that both outline and node list forms are accepted here just as in the sibling, which is useful but modest.

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?

States a specific verb and resource ("checked" / "decision tree") and immediately scopes it as validation-only ("without a drawing"). It explicitly contrasts with make_decision_tree by saying it takes the same input but does not draw or create files, so an agent can distinguish the two without reading either schema.

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?

Gives concrete trigger phrasing the user might say ("do my probabilities add up to 1", "is anything missing in this tree") plus an explicit exclusion: "Do not use it to estimate probabilities" and "Does not draw the tree or make files." The alternative sibling is named and the relationship (same inputs, different output) is spelled out.

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

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds substantial behavior beyond them: fractional rollback semantics, tie listing, branch-cost rule, validation refusals naming the offending node and sum, a 300-node cap, the concrete return artifacts, and the ~1 hour expiry on large-tree download links. That is exactly the operational context annotations cannot carry.

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?

It is a single dense paragraph but front-loaded with the trigger condition and free of filler; each sentence adds scope, input, output or a limit. The size is justified by the tool's complexity, though a short bulleted split of inputs vs. guarantees would scan faster.

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?

For a 7-parameter combinatorial tool with an output schema, the description covers input modes, limits (300 nodes), failure modes, output artifacts and link expiry. Nothing an agent needs in order to call it correctly appears to be missing.

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?

Schema coverage is 100%, so the baseline is 3; the description earns one more by framing outline and nodes as alternative input modes for the same tree (an either/or relationship the schema lists as two independent properties). It also flags that costs are applied 'by a stated rule', though it does not state the rule, leaving some ambiguity.

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?

The description states a specific verb+resource pair ('decision tree drawn or solved') and enumerates the concrete inputs and outputs (rollback with expected values, chosen option, PDF/SVG/CSV). It never names the sibling check_tree, so an agent must infer the boundary between 'make' and 'check' on its own, which keeps this out of the top band.

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?

It gives explicit trigger phrases ('which option has the higher expected value', 'draw a decision tree for launching this product') and a clear scope exclusion ('does not estimate probabilities or give advice'). It does not name alternatives or say when to prefer check_tree instead, and the built-in validation refusal partially overlaps what a check tool would do.

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