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

No annotations exist, so the description carries the full behavioral burden and does so richly: free with rate limits (500 rows/50 columns per call), determinism guarantee ('same schema and seed give byte-identical output for the same generator version'), the x-generator-version header, and the output_path threshold for large results. The data-provenance constraint ('Send schemas only, never real data') is exactly the kind of safety context an agent cannot get from 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?

Front-loads purpose and the primary usage directive, then layers determinism, safety and output guidance. Dense but nearly every sentence carries a non-obvious fact; only the closing curl/sha256 sentence feels like it could be trimmed since it describes result shape.

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?

A 9-parameter tool with nested objects, no annotations and no output schema — the description compensates on every front: safety profile, rate limits, determinism, file-vs-return behavior, and even the shape of the result (sha256 + regenerating curl). An agent has everything needed to call it correctly on the first attempt.

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 baseline is 3. The description still adds meaning: it explains when to prefer schema over columns (Pydantic/Zod export, OpenAPI component), notes unmappable fields are named, and gives the practical threshold for output_path ('more than about 50 rows'). It stops short of explaining the chaos object or the columns type vocabulary, which stays in the 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+resource with output formats ('Generate realistic fake test data (CSV, JSON or SQL INSERTs) from a table schema'). It differentiates from siblings by name — list_column_types for type options, invalid_values and the sandbox_* tools for downstream steps — so an agent can route without opening schemas.

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?

Explicit when-to-use ('Use it first, when you need rows for fixtures, seed data or tests') plus a named sequencing of alternatives: list_column_types for options, then invalid_values, then sandbox_create/sandbox_payments. Also gives an alternative input mode (schema instead of columns), leaving nothing to inference.

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 exist, so the description carries the full burden, and it does disclose two important traits beyond the schema: determinism ('Same seed, same output for the same catalogue version') and the output shape ('each with the reason it is invalid'). It does not state that the call is side-effect-free or whether any auth is needed, which leaves a small gap for a generation tool.

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, all front-loaded: purpose first, then the reason field, then determinism, then placement in the workflow. No sentence repeats the name or restates the schema.

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?

With 5 parameters including a nested chaos object and no output schema, the description supplies purpose, determinism, and workflow position, which is most of what an agent needs. The chaos behaviour (failure injection, latency, empty results) is only discoverable in the schema, so the description is not fully self-sufficient for error-handling tests.

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 80%, so the baseline is 3; the description goes beyond it by naming the concrete value families behind the kind enum (email, card, address, unicode, iban, ssn) and by explaining that 'reason' accompanies each value via the fields filter example. The nested chaos object and its injection semantics are left entirely to the 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 resource and a distinguishing modifier: 'Deliberately invalid TEST DATA values', enumerating the categories (malformed emails, Luhn-invalid cards, Unicode edge cases, IBAN/SSN shapes). This clearly separates it from the sibling generate_test_data, which produces valid data.

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 explicit sequencing: 'Use it after generate_test_data, to test that your validation rejects bad input. Next: sandbox_create, then sandbox_payments, to test a payment flow.' Both the triggering condition and the downstream alternatives are named, so routing is unambiguous.

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. "List" implies a read-only lookup with no side effects, and it says the result contains types and their accepted options, but there is no disclosure of permissions, pagination, or return format. Adequate for a zero-argument read tool, yet thin on behavioral detail.

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 short sentences, nothing redundant: the first says what is returned, the second says when to call it. Front-loaded and fully earned.

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 lookup with no output schema, the description tells the agent both the content (types and their options) and the calling context (before generate_test_data). Slightly more detail on the shape of the returned options would close the remaining gap, but nothing essential 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?

The tool takes zero parameters, so per the rubric this sits at baseline 4. The description does not need to explain argument semantics and adds no contradicting information.

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 ("List the supported column types and the options each accepts") and explicitly relates itself to the sibling generate_test_data, so an agent can separate this lookup tool from data-generation tools. It does not, however, clarify the domain scope of "column types" beyond the sibling reference.

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 an explicit trigger — "Use it before generate_test_data when you need a type's options" — naming both the alternative and the condition that selects this tool. No when-not guidance or mention of other siblings (invalid_values, sandbox_*), but the routing intent 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, the description carries the full burden and does well: it discloses that the data is fake/no real payment provider, that it returns an sbx_ id, and crucially a lifecycle constraint ('Deleted 1 hour after creation'). Gaps remain around authorization requirements and whether the call is idempotent or rate-limited.

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?

Four short clauses, all front-loaded: what it creates, its fake nature, the returned id, the 1-hour TTL, and the next step. Zero 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?

No output schema exists, but the description names the return value (sbx_ id) and the TTL, covering what the agent needs to chain calls. The only meaningful omission is any explanation of the 'seed' parameter, which is left undocumented.

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 'seed' parameter, and the description never mentions it, so nothing explains what a seed controls (e.g., deterministic card/test data generation). The description fails to compensate for the schema 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?

States a specific verb+resource ('Create a fake card-processor sandbox') and immediately frames its scope vs. real infrastructure ('TEST DATA, no money, no payment provider'). An agent can distinguish it from generate_test_data or sandbox_payments without opening any 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 clear sequencing: 'Use it to start a payment test; next, call sandbox_payments with the returned id,' which routes the agent to the correct sibling. It does not, however, state when NOT to use this tool or contrast it with the other siblings (generate_test_data, invalid_values).

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 and delivers unusually strong content: card-number acceptance restricted to Stripe's published test list, declines exposing both code and decline_code, and deterministic replay given the same seed and call order. The notable gap is that 'reset' is a clearly destructive operation and nothing warns about what it wipes or that it is irreversible.

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?

Five short sentences, front-loaded with the operation set and then layered with constraints in descending order of importance. Every sentence carries distinct information; none restates the title or the schema.

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 10-parameter tool with a nested chaos object, no annotations, and no output schema, the description covers behavior and sequencing well but omits the chaos/testing-failure surface and any notion of result shape or pagination for list. An agent can invoke the common paths correctly but would be guessing on the testing and query-modifier parameters.

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?

At 50% schema coverage across 10 parameters, the description compensates partially: it explains the semantics of card and sandbox_id and ties each verb to the action enum. It says nothing about chaos (a nested object of deliberate misbehaviour), limit, fields, currency, reference, or amount, so several parameters remain documented only, or not at all, in the 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 opening clause names the resource (payment sandbox) and enumerates every supported operation (charge, refund, list, get, reset), which maps directly onto the action enum. It is immediately distinguishable from siblings like sandbox_create and invalid_values, which are not sandbox-payment 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?

It gives an explicit prerequisite and ordering rule: 'Use it after sandbox_create, with that sandbox's id.' That is concrete routing guidance. It stops short of stating when NOT to use it (e.g., against live payments, or when invalid_values / generate_test_data is the right sibling), so it is clear context without exclusions.

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