Clean Table

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

The description goes beyond annotations by disclosing deterministic behavior, the re-checking of cells against source tokens, and the never-guess policy with exceptions. While annotations indicate read-only and idempotent, the description adds valuable behavioral context about data validation and payment necessity. However, it doesn't fully detail edge cases like how it handles conflicting date formats, but the given information is already rich.

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 well-structured and front-loaded with the primary use case, followed by operational details and payment info. While it is slightly lengthy, every sentence carries necessary information (normalization operations, exception handling, determinism, payment) and avoids fluff. The structure is logical and easy to scan.

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 tool's moderate complexity and the fact that there is no output schema, the description covers all critical aspects: what the tool transforms, how it handles unreadable data, determinism, and payment requirements. It also clarifies the order of date interpretation. Remaining gaps include specifics of the exception format and exact response structure, but those aren't essential for initial invocation. It is complete enough for an agent to decide and execute correctly.

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. The description adds semantics beyond the schema by explaining the overall purpose of parameters (e.g., 'fields' as the agreed schema with type conversions, 'dateOrder' with the file's proof override). This enriches the schema details with practical context on how parameters affect behavior, pushing the score to 4.

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's function: conforms a delimited table to a user-stated schema, with explicit detail on normalization operations (dates to ISO, money to numbers, text collapsed, optional dedup). It also clearly distinguishes itself from schema-inference tools by stating it does not infer the schema for you. This is a specific verb-resource pair with no 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 explicitly states when to use the tool: when you have a CSV and already know which source column becomes which output field. It also implies when not to use it (when you need schema inference) and mentions that the file's own date order proof wins over the provided dateOrder, guiding usage. The payment requirement is also a usage trigger.

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?

Annotations already mark the tool as read-only, and the description adds meaningful context: it is a free health/status endpoint, includes a defined set of information, and explicitly states 'No secrets.' This goes beyond the annotation without contradicting it, though it does not detail response format or potential limitations.

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 short sentences with no filler. The core purpose is front-loaded, and every phrase adds value, including the security reassurance 'No secrets.'

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 parameterless, read-only status tool, the description is complete: it states what the tool returns, covers the free/paid dimension, and notes the absence of secrets. No output schema exists, but the enumerated content gives an agent enough to decide whether to call 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?

The tool has zero parameters, so the input schema is trivially complete. Per the baseline for parameterless tools, the description need not add parameter-level detail, and it appropriately focuses on the output instead.

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 names a specific resource ('service status') and enumerates its fields (version, environment, network, autonomy mode, paid offer), making its purpose immediately clear. The scope is distinct from the sibling clean_table, so an agent can tell them apart without further investigation.

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 the tool is for health/status checks, but it does not explicitly state when to use it versus alternatives or provide exclusion criteria. The sibling clean_table is unrelated, so there is no direct comparison, leaving usage context mostly implicit.

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