Bay Run

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

With no annotations, the description carries the full burden, and it pays off: it reveals execution order, conditional behavior (Rerank only after SAFE), output contents, default omit_raw_result, and absence of text generation/tool execution. It even states auth/pricing conditions with the key endpoint, which is valuable context.

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 definition front-loads purpose and order, then covers default behavior, side effects, and access. It is slightly long due to pricing/auth copy, but each sentence earns its place.

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?

The key details an agent needs are present: required user_text, optional documents, default omit_raw_result=true, the no-generation/no-tool-execution guarantee, and the key endpoint. Because an output schema exists, the return-value descriptions are a bonus rather than a gap; only explicit failure/blocked behavior is left implicit.

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?

All three parameters have full schema descriptions, so the baseline is 3. The description repeats the same semantics already in the input schema—e.g., omit_raw_result default and the compatibility opt-in—but does not materially extend them.

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 first sentence identifies a specific behavior: a bounded Guard-first composition that runs the Guard Pin then the Rerank Pin only after SAFE. It also explicitly states what it never does (generates text, executes tools), which distinguishes it from siblings like run_pin and solve_task.

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 implies when to use it—when a Guard-then-Rerank pipeline is needed and the caller will handle subsequent steps—and notes the caller owns next steps. However, it never explicitly contrasts it with run_pin or solve_task, nor gives a condition for choosing one over the others.

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 and far exceeds it: it discloses the decision-first default, that max_price_usd is a hard ceiling 'never a promise to spend', batch no-spend evidence caveats, deterministic auto-minted idempotency with replay semantics, and that alias responses return the underlying durable pin_id with execution_id and feedback templates. No annotation contradiction exists since no annotations were provided.

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 one unbroken ~350-word paragraph mixing core semantics, workflow advice, pricing policy, retention auth, canonical route IDs, and promotional copy ('Free during launch: no card, no signup, no email') with no bullet or section structure. Nearly every sentence carries information, but the lack of organization and inclusion of marketing noise make it materially harder for an agent 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?

For a 5-param tool with an output schema and zero annotations, the description covers invocation, batch behavior, optional-parameter semantics, pricing, authentication (key-minting URL, evaluation:owner_write for retention), and result shape attributes. Nothing an agent needs to call it correctly is 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, but the description adds substantial non-obvious meaning: the bounded list of up to 8 strings for Guard/classification pins, mint-ceiling inheritance when max_price_usd is omitted, auto-minted idempotency keys from {pin_id, input}, and the condition to 'set it false only when the receipt-bound raw result is required'. These semantics are essential for correct invocation and go well beyond the schema entries.

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 clear verb+resource: 'Run a public Bay Run Pin with just its input — {pin_id, input}', and expands with canonical aliases (guard, sentiment, tickets, rerank) and concrete route IDs, so an agent knows exactly what the tool executes. However, it never differentiates from its siblings coprocessor and solve_task, so it stops short of a 5.

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 explicit workflow context: 'run the Guard Pin before generation or tool use, then run the Rerank Pin on retrieved documents before adding them to context' and 'For canonical Pins, act on decision.action first'. This is actionable situational guidance, but it does not state when to prefer run_pin over the sibling tools or when not to use it.

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?

No annotations are provided, so the description carries the full disclosure burden. It reveals important behavioral traits: special canonical-route reuse, refusal to invent urgency/severity taxonomies, no remapping of caller-defined labels, and a free-launch no-signup key minting flow. It could add error or response behavior details, but the auth and routing disclosures are substantive.

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, but the description is a dense run-on paragraph repeated almost verbatim in the task_description schema field. The support-ticket canonical-route sentence appears twice, which adds redundancy. It is informative but could be tightened without losing meaning.

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?

There is an output schema, so return-value documentation is not the description's burden. The description covers task routing, taxonomy constraints, refusal behavior, and the free key minting endpoint, which is strong for a tool with no annotations and 10 optional parameters. It is slightly less complete on explicit sibling-tool differentiation, but overall the operational context is well supplied.

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%, so the baseline is 3 even without extra parameter explanation. The description does add some semantic context, such as the canonical route behavior for task_description and the requirement that urgency/severity classification include candidate_labels or examples. Most parameter meaning, though, is already fully carried by the schema's alias and input descriptions.

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 and resource: 'Route and execute one task-shaped call.' It immediately distinguishes itself from run_pin by stating the caller does not need to learn 'quote/run shapes,' and it names concrete use cases like support-ticket routing/triage. This is much stronger than a generic 'solve a task' statement.

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 gives clear routing conditions: support-ticket triage reuses a canonical Pin route when taxonomy is omitted or exactly access_issue, billing_issue, and delivery_issue; other classifications stay on their requested path; urgency/severity/priority require candidate_labels or labeled examples. It does not explicitly name sibling tools as alternatives, but the 'without learning quote/run shapes' phrasing implies when solve_task is preferable to lower-level Pin calls.

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