Second Opinion

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

With no annotations provided, the description carries full burden. It discloses adversarial verification, cost ($0.75), time (~90s), vote outcome, and quote behavior without payment. However, it omits details like exact return format with payment, idempotency, or error handling, leaving moderate gaps.

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 two sentences, front-loading key facts (verifiers, cost, time) and usage guidance. It efficiently covers purpose and exceptions, though could benefit from separating quoted vs. paid behavior more clearly.

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 no output schema and moderate complexity, the description provides core purpose, usage context, and payment behavior. However, it lacks details about the vote result format or integration with sibling tools like second_opinion_result, which may leave agents uncertain about post-payment workflow.

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 baseline is 3. The description adds behavioral context for the payment parameter (quote vs. execution) and clarifies claim/context purposes beyond schema descriptions. It adds value but does not dramatically elevate understanding.

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 clearly states the tool uses three independent adversarial verifiers (evidence, logic, currency) to attack a claim and vote, with cost/time details. It distinguishes from siblings by specifying use for high-stakes claims where a single check isn't enough, providing a specific verb-resource pair and differentiation.

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 explicitly recommends use for high-stakes or contested claims and mentions alternative behavior without payment (returns quote). However, it does not explicitly state when not to use or name alternative tools like second_opinion_quick, though the implication is clear.

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 bears full responsibility for behavioral disclosure. It describes the output as 'current tiers, prices, and exact on-chain payment requirements (accepts)', which informs the agent of the data it will receive. While it does not explicitly state read-only behavior, the nature of the tool (pricing query) and the lack of parameters imply no side effects.

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 a single sentence that efficiently conveys the tool's purpose and usage. It front-loads 'FREE' and uses a clear list of rails. Every clause adds value without redundancy.

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 no parameters and no output schema, the description provides sufficient information about what it returns. It explains the key aspects: pricing tiers, exact payment requirements, and per-rail coverage. It could be more specific about the output format, but overall it is complete enough for an agent to understand its capability.

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 no parameters and the schema is empty (100% coverage). The description adds full meaning by explaining that the tool returns pricing information for each rail. Since no parameters exist, the description effectively conveys what the tool does without needing parameter details.

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 clearly identifies the tool as providing current pricing tiers and on-chain payment requirements for specific blockchain rails (Algorand, Base, Solana). It uses a specific verb ('discover') and resource ('pricing'), and the scope is well-defined, differentiating it from sibling tools like second_opinion_verify or second_opinion_result.

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 explicitly states two use cases: pre-building a payment and discovering costs before committing. This gives clear context for when to use the tool. However, it does not explicitly mention when not to use it or compare it to alternatives, which prevents a perfect score.

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?

Discloses cost ($0.03), timing (~20s), no web research, output types (refuted/supported/inconclusive with calibrated confidence), and escalation logic. No annotations exist, so description fully covers behavioral traits without contradiction.

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?

Every sentence earns its place. The description is front-loaded with purpose, then usage, then behavior, then parameter details. No wasted words despite covering many aspects.

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 no annotations, no output schema, and 3 parameters, the description is exceptionally complete. It explains what the tool does, when to use it, behavioral details, parameter meanings, and fallback behavior. Leaves no gaps.

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%, but description adds significant value: explains the purpose of 'claim' and 'context', and gives detailed guidance on 'payment' including the quote behavior. Goes beyond schema.

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 clearly states the tool is an 'independent reasoning-only check of a claim' with specific use cases like logic, internal consistency, and stable knowledge. It distinguishes itself from siblings by mentioning escalation to second_opinion_verify for current-world research.

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?

Explicitly states when to use (logic, internal consistency, stable knowledge) and when not to (claims needing current-world research). Also explains behavior without payment (quote). Provides clear alternatives via sibling tool.

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?

Discloses return values (status and full verdict when done) and cost implications (free polling). However, lacks details on potential errors, latency expectations, or authorization requirements. With no annotations, the description should be more comprehensive.

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?

Single front-loaded sentence that immediately conveys purpose and key details. No wasted words; every part adds value.

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 the simplicity of the tool (one param, no output schema), the description is adequate. It covers what the tool returns and cost aspects. Could be more explicit about the source of the job_id, but the schema already addresses that.

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?

Only one parameter 'job_id', and the schema already provides a description. The description adds no additional meaning beyond what the schema states. Schema coverage is 100%, so baseline 3 is appropriate.

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 fetches the verdict for a paid async job. The verb 'Fetch' and resource 'verdict' are specific. Distinguishes from sibling tools by focusing on result retrieval.

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 context that polling is free and the verdict is already paid for, implying use after a paid job submission. Could explicitly mention when to use (after calling a paid verification tool) but current guidance is clear enough.

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?

Discloses cost ($0.20), duration (~60s), adversarial process, and quote mechanism. With no annotations, it sufficiently covers the tool's non-destructive nature and expected outputs.

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?

Three sentences: overview, use case, payment behavior. Front-loaded and free of redundancy. Every sentence adds essential information.

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?

Adequate for a 4-param tool with no output schema. Explains purpose, cost, timing, and optional payment. Could briefly describe verdict structure (cite format), but overall complete enough for selection and invocation.

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 covers all parameters with descriptions (100%). The description adds key insight: omitting payment returns a quote, and claimed_sources are scrutinized not trusted. This enriches parameter understanding.

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 clearly states the tool performs 'independent adversarial verification' of a single claim using web research, returning a verdict with citations. It distinguishes itself from siblings like 'second_opinion_quick' by emphasizing thoroughness and cost.

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?

Explicitly advises use 'before irreversible commitments that depend on current-world facts'. Explains quote behavior without payment. Lacks direct comparison to sibling tools but context is clear.

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