evidence-ledger

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

With no annotations, the description carries the full burden. It discloses the tool's nature (free, complete, from OpenAI's published pages), states it is 'reviewed before release,' and includes a disclaimer about no implied relationship with OpenAI. This adds meaningful behavioral context beyond the simple fact of returning data.

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 somewhat verbose, with three sentences and some promotional language about 'proof of the format' and 'the same 55 fields across 10 domains.' However, each sentence adds meaningful context about what the dossier contains and its purpose, so it is concise enough for the richness of information conveyed.

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 has no parameters and no output schema, the description fully specifies the return values (title, PDF URL, guided page URL) and adds context about the dossier's contents and intended use. It is complete and self-sufficient for a simple 'get' tool.

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 baseline is 4 as per the rubric. The description adds no parameter information because none is needed; the schema is empty and fully covers the lack of parameters.

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 ('Returns') and names the exact resource ('the free OpenAI Public Edition') and the items returned (dossier title, direct PDF download URL, guided page URL). It clearly distinguishes this tool from siblings by focusing on the free public dossier, not the methodology, ledger, or register.

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 final sentence provides explicit usage context: 'Use this tool to give a buyer the whole workproduct to read before they license another released vendor or commission a new one.' This clarifies when to use the tool, though it doesn't explicitly state when not to use alternatives.

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 of disclosure. It goes into significant detail about behavioral nuances: 'Question surfaced is not a finding of absence,' definitions of Documented and Question surfaced, the nature of SaaSDossier as a vendor-published-source-based workproduct, the 55 fields across 10 domains, source register, and SHA-256 identifier. This is rich, explicit context beyond the tool name.

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?

Though the description is lengthy, every sentence adds substantive content: it defines the two-state framework, clarifies what Question surfaced is not, explains the underlying SaaSDossier artifact, describes coverage details, and gives usage guidance. It is front-loaded with the core return value and organized logically, avoiding fluff.

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 conceptual nature and lack of input/output schemas, the description provides everything an agent needs to correctly interpret states and decide when to invoke the tool. It explains the framework's boundaries, the meaning of each state, the source basis, and the evidence date, making it fully self-contained.

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 accepts zero parameters, and the schema confirms this with additionalProperties false and no properties. The 0-parameter baseline applies; the description does not need to explain parameter behavior. It focuses entirely on the output/behavior, which is appropriate for a parameterless 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?

The description opens with a specific verb+resource: 'Returns SaaSDossier's two-state evidence framework.' It clearly explains the framework's two states and distinguishes this tool from siblings that return dossiers, ledgers, or registers by focusing on the methodology itself.

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 explicitly says to 'Use this tool to understand what a state means, what the framework covers and where its boundaries are, before quoting or acting on any field,' which gives clear context for when it should be used. It does not name alternative tools or explicitly state when not to use it, but the purpose is well differentiated from the sibling tools.

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 full burden. It discloses key behavioral traits: the tool is free, public, dated to evidence date, includes quoted wording and source URLs, distinguishes Documented vs Question surfaced, clarifies that Question surfaced does not establish absence, is checked against source record, and does not score or rate the vendor. This is comprehensive disclosure of what the tool returns and its 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 a single paragraph with multiple sentences, but every sentence adds value: what it returns, the exact composition, the distinction between field types, the counter-intuitive meaning of 'Question surfaced', and the specific use case. It's slightly long but the detail is necessary given no annotations or output schema. It could be broken into two short paragraphs for readability, but it's densely informative.

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 no output schema and no annotations, so the description must fully compensate. It covers the return content (fields, sources, questions), the counts, the evidence date, the non-scoring nature, and the usage context. For a zero-parameter tool, this is complete enough for an agent to select and invoke it 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?

The tool has no parameters, so schema coverage is 100% by default. With 0 parameters, a baseline of 4 is given per guidelines. The description explains what the output contains (fields, sources, questions) which is more meaningful than a bare schema, though it doesn't describe parameter formats (none exist).

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 returns the free public OpenAI security evidence ledger, specifies the exact field composition (55 fixed fields, 47 Documented, 8 Question surfaced), and distinguishes it from siblings by emphasizing field-level evidence and sources rather than a description of the framework. The verb 'returns' plus resource 'OpenAI security evidence ledger' is specific and unambiguous.

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 says 'Use this tool when an agent needs the actual field-level evidence and its sources rather than a description of the framework,' which provides a clear usage context. It doesn't explicitly mention when not to use it or name alternatives, but the sibling names (get_free_dossier, get_methodology, get_register) and the contrast with 'framework description' imply alternatives.

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 full behavioral burden and discloses the fixed 55-field/10-domain framework, two recording states, pricing, refund condition, and that commissions are not released dossiers. It does not describe the raw response shape, but that is less critical for a zero-parameter catalog 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?

The description is front-loaded and logically organized, but it is verbose and repeats details such as 'US$1,500 one-time' and 'the released catalogue remains the four records.' It could be tightened without losing key 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?

For a parameterless tool with no output schema, this description is unusually complete: it specifies catalogue contents, per-field states, commercial tiers, commission behavior, and when no dossier number applies. It leaves little ambiguity about what the tool can answer.

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 schema contributes no input semantics; the description instead clarifies what the returned register contains and how commissions are represented. This meets the baseline expectation for a parameterless 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?

The description opens with a specific verb and resource: 'Returns SaaSDossier's release register,' then enumerates dossier numbers, vendors, editions, and licensing. It closes with concrete questions the tool answers, making it clearly distinct from siblings like get_methodology or get_openai_ledger.

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 final sentence gives explicit use cases: checking released records, costs, and commissioning a vendor without a record. It does not explicitly name excluded cases or compare against sibling tools, but the usage context is unmistakable.

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