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

With no annotations, the description carries the disclosure burden and does well: it explains the privacy model (fresh zero-knowledge key, gateway cannot identify the asker) and the cost behavior (spends the model's price in questions). It omits error behavior, rate limits, and return details, but adds substantial behavioral context beyond the schema.

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 short sentences, front-loaded with the action and followed by the two decision-relevant traits: privacy and cost. Every sentence adds useful information without waste.

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 the key considerations for this paid private query tool: privacy guarantees, question spending, and where to find model IDs. Since there is no output schema, it could explicitly state that the model's answer is returned or describe insufficient-balance behavior, but overall it is complete enough for correct 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 coverage is 100%, so the baseline is 3. The description reinforces that model IDs come from the models tool, but it does not add syntax or meaning beyond what the schema already provides for question, model, or system.

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: 'Ask one of PARANOID's AI models a question,' with the additional qualifier 'in private.' This clearly distinguishes it from sibling tools like models, balance, and buy, which are listing/pricing/purchasing operations.

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 primary use case is clear: ask a model a question privately. It also points to the models tool for model IDs/pricing, giving useful context. However, it does not explicitly name the buy or balance alternatives or state when not to use this tool, so it falls short of a 5.

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 the full behavioral burden. It discloses that the result is membership-scoped and time-limited, but does not explicitly state that the operation is read-only, requires authentication, consumes no questions, or describes the response format.

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?

A single, efficient sentence that front-loads the key returned information. There is no wasted 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 zero-parameter query with no output schema and no annotations, the description sufficiently explains the return values: remaining questions and expiration date. Minor gap is lack of explicit authentication or current-membership context.

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, so there is no parameter semantics to explain. Baseline score of 4 is appropriate for a no-parameter tool.

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 resource and returned information: remaining question count and last usable day for this membership. It is specific enough to distinguish from siblings like ask, buy, and models, though it lacks an explicit verb such as 'returns'.

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?

Usage is implied: check remaining question quota and expiration, likely before calling ask or buy. However, it does not explicitly say when to use this tool versus alternatives or mention prerequisites.

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 the full burden and does real work: it discloses that this is a real-money purchase, the exact unit price and setup fee, the chain and currency, and — importantly — that an existing membership with remaining questions is preserved rather than overwritten. It stops short of stating irreversibility, failure/refund behavior, or expected latency for an on-chain payment.

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 dense sentences, front-loaded with the action and cost — the most decision-relevant facts come first. The closing clause 'kept in a file beside the new one' is vaguely worded and costs a re-read, which keeps it from a 5.

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 an unannotated, irreversible spend operation with no output schema, the pricing and state-preservation details are valuable, but gaps remain: what a successful call returns (no output schema to fall back on), what happens if payment fails mid-transaction, and how the payer wallet is supplied. An agent could call this correctly but not predict the result.

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 and schema coverage is 100%, so the schema already explains 'questions' fully. The description corroborates the minimum ('250 questions at least') and adds per-question pricing that lets an agent compute total cost, but adds no further semantics beyond that 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 uses a specific verb and resource — 'Buy a new membership' — and qualifies it with the payment rail (x402 in USDC on Solana). It is clearly distinguishable from ask/balance/models by implication, though it never names a sibling or states the boundary explicitly.

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?

Usage is only implied: an agent infers 'call this when you need questions' from the '250 questions at least' clause. There is no guidance on checking balance first, no prerequisites (e.g., needing SOL for fees or how PARANOID_PAYER is configured), and no statement of when not to buy.

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 burden, but the tool takes zero parameters and is an obvious read-only listing. The one genuinely useful behavioral disclosure is that cost is expressed per single message/question sent to each model. It does not state auth or quota requirements, pagination, or ordering of results.

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?

A single front-loaded sentence with the verb first and no filler. Every clause carries information: what is listed and what extra attribute accompanies each item.

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 simple no-parameter, no-output-schema listing tool, the description covers both the collection returned and the key per-item attribute (cost). Only minor gaps remain, such as the shape of the returned list or whether identifiers from it feed into 'ask'.

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, so there is no parameter semantics to explain; the baseline for a no-param tool applies. Nothing in the description contradicts or muddies the empty 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?

States a specific verb ('List') and resource ('models') and adds the distinctive payload detail: the per-message question cost for each model. This is unmistakably different from the siblings ask/balance/buy, though it never explicitly names or rules out an alternative.

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?

There is no explicit when-to-use or when-not-to-use guidance. The mention of per-message cost implies the tool is a lookup to consult before choosing a model for 'ask', but the agent must infer that workflow rather than being told.

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