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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It precisely outlines the return structure (an object with 'names' and 'plannable' fields) and elaborates on the meaning of 'plannable' (award programs with priced charts plus bank currencies that transfer into one). This gives the agent solid insight into what the tool returns and on what basis, well beyond a generic 'list' statement.

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?

The description is three concise sentences: the first states the purpose, the second defines the return shape, and the third explains the 'plannable' field and its intended use. Every sentence contributes meaningful information with no redundancy or padding, making it efficiently structured and easy to parse.

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?

Given that the tool has no parameters and no output schema, the description is exceptionally complete. It fully defines the return object, explains the semantics of each field, and explicitly ties the output to the sibling tool solve_award_trip. There are no critical gaps that would prevent an agent from correctly invoking the tool and interpreting its results.

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?

The tool has zero parameters and the input schema is empty, so the description has no parameter semantics to add. Per the rubric, a tool with zero parameters gets a baseline of 4. The description appropriately focuses on output and usage rather than parameters, so the score reflects this baseline.

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 begins with 'List every rewards program AwardMath tracks,' which is a clear verb and resource definition. It does not explicitly differentiate from sibling tools like parse_wallet or solve_award_trip, but the listing nature is unmistakable given the name and the different purposes of those siblings. Thus it is specific but lacks explicit sibling distinction.

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?

The description goes beyond just stating what it does by explaining when to use the output: 'Use these ids in solve_award_trip wallets.' This directly connects the tool to a sibling and prescribes how the returned 'plannable' ids should be consumed. It also clarifies which programs qualify as 'plannable,' giving the agent a clear decision rule for usage.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it delivers: it spells out the return shape (balances, unrecognized, needs_clarification), and crucially states the no-guessing policy — 'Anything ambiguous lands in the latter two lists instead of being guessed — show it to the user rather than dropping it.' This is a meaningful behavioral trait. It stops short of edge-case handling (e.g., empty text), keeping it from a 5.

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?

The description is front-loaded with the purpose, followed by a compact example, the return schema, and the behavioral contract. Each sentence earns its place. It runs slightly long due to the dual example-and-schema-format block, but nothing is redundant.

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 single-parameter parse tool with no output schema, the description is nearly complete: it states the input, enumerates the three output lists, and defines the ambiguity contract. The absence of an output schema means the description must explain return values, which it does. Minor omission: it doesn't state a reasonable fallback for degenerate input, but the tool's complexity doesn't demand it.

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 0% for the single required parameter, so the description must compensate. The example input ('I have 250k Amex MR, 80k Chase...') conveys the expected free-text format with program names and amounts, which is the main semantic need. However, the description never explicitly defines the parameter's contract beyond the example — it leaves the agent to infer that text is a raw user message about point balances.

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 opening line 'Turn free text about points balances into structured wallet entries' uses a specific verb (turn/parse), a defined resource (free text about points balances), and a concrete outcome (structured wallet entries). It clearly differentiates from siblings list_programs and solve_award_trip, which serve materially different purposes.

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?

The description provides an explicit when-not-to-use condition: 'If you already know the balances in structured form, skip this and build the wallet directly.' This tells an agent precisely when the tool is unnecessary. It implies when to use it (when balances exist only as free text) via the example, though it doesn't formally exclude siblings — acceptable since the siblings are not genuine alternatives to a text-parsing operation.

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?

With no annotations, the description carries full responsibility. It explicitly states plans are priced from published charts and never assert live seat availability, and that empty results include no_plan_reasons. This is a strong disclosure of key behavioral limitations and return expectations, though it doesn't cover every potential edge case.

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?

The purpose is front-loaded, followed by a systematic per-parameter breakdown and behavioral caveats. The text is longer than average but every sentence adds value; there is no fluff or redundant wording.

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 tool with 8 parameters, no annotations, and no output schema, the description covers purpose, inputs, high-level output (plans with transfer steps, points math, fees, no_plan_reasons), and a critical limitation (no live availability). It does not describe the exact output structure, but the summary is sufficient for an agent to call and interpret results.

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 0%, so the description must compensate. It explains wallet with an example, origins/destinations with IATA codes, cabins with options and default, max_cash_usd as a cap, and window dates as ISO optional. It covers 7 of 8 parameters but omits 'travelers', leaving its meaning to inference—a minor but notable gap.

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 opens with a specific verb-resource pair ('Compute up to three verified award-booking plans for a trip') and the context clearly distinguishes it from siblings list_programs and parse_wallet. The 'verified' and 'up to three' qualifiers add specificity without ambiguity.

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?

The description provides clear context for when to use the tool: after obtaining wallet IDs from list_programs, and it instructs the agent to confirm live availability before irreversible transfers. It does not explicitly name alternatives or exclusions, but the sibling relationships are implicit, so this is adequate.

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