anteroom

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

With no annotations, the description carries the full burden. It explicitly states it returns 'every published version, effective date, published changelog, and primary source URL', indicating a read-only, comprehensive retrieval. However, it does not mention potential pagination or rate limits, which are minor omissions.

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 the main action and listing return fields without redundancy. Every sentence 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 simple input (one parameter) and no output schema, the description thoroughly covers what the tool does and what it returns. It is complete for an agent to use 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 schema for lab_slug already provides the valid values from the description, achieving 100% coverage. The tool description adds context about what the returned data includes, enhancing the parameter's purpose beyond the schema alone.

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 verb 'Get' and the resource 'full version ledger of a frontier AI lab's safety framework', specifying exactly what is returned (published versions, effective dates, changelogs, source URLs). It distinguishes itself from sibling tools like get_partnership_pattern or search_corpus, which deal with different domains.

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?

While no explicit when-to-use or alternatives are provided, the tool's purpose is self-contained and siblings are tangentially related, making usage context clear. The absence of exclusions is acceptable given the tool's simplicity.

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 must handle behavioral transparency. It lists the types of information returned (plain-language description, example clauses, etc.), which is helpful, but it does not disclose error behavior (e.g., if pattern_slug is invalid), authentication needs, rate limits, or whether the operation is read-only. The description is adequate but not exhaustive.

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 what it returns. It is front-loaded with the action and resource, and every part adds information. It could be formatted with bullets for readability, but as is it is concise and clear.

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 simplicity (one parameter, no output schema), the description is fairly complete: it specifies the input and the nature of the output. However, it could be improved by noting the output format (e.g., JSON) and what happens on invalid slugs. Considering the high schema coverage, it achieves reasonable completeness.

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 adds value by providing examples of valid pattern_slug values (e.g., 'compute-backed-strategic-invest'), which clarify the expected input format beyond the schema's 'string' type. This justifies a score above 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 clearly states the verb 'Get' and the resource 'named strategic AI partnership pattern', and lists the specific contents returned. It distinguishes from siblings like 'get_partnerships_by_pattern' which likely returns multiple patterns, making the tool's unique purpose evident.

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 usage for retrieving a specific pattern's details, but does not explicitly state when to use this tool instead of alternatives like 'get_partnerships_by_pattern' or what prerequisites exist. The context from sibling tools provides hints, but the description lacks explicit guidance.

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 clearly conveys the read-only nature (listing) and return value (metadata with permalink). No contradictions or hidden behaviors.

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 sentences, front-loaded with action verb and resource, no unnecessary words. Efficient and clear.

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 simplicity (2 params, no output schema) and schema coverage, the description is complete enough for an agent. Missing guidance on when to use vs siblings, but still functional.

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% with both parameters described. The description adds minimal extra meaning, just mentioning 'given pattern' for pattern_slug, which is already in the schema description.

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 lists partnerships by pattern, specifies 'tracked public AI strategic partnerships' classified by 'Anteroom', and mentions returning metadata with permalink. It distinguishes from siblings like 'get_partnership_pattern' which returns pattern definitions.

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 use for listing partnerships of a given pattern but does not provide explicit when-to-use or when-not-to-use guidance or mention alternative tools like 'get_partnership_pattern' for pattern discovery.

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 full burden. It details the return structure (quoted text, source URL, effective date) and lists all seven commitment categories, providing strong transparency for a read operation.

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 concise sentences with no wasted words. The first sentence front-loads the core purpose and enumeration of commitments, and the second describes the return fields.

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 one parameter and no output schema, the description sufficiently explains input (including valid slugs) and output structure (quoted text, source URL, effective date). It lacks mention of pagination or error handling, but for a simple list tool, this is adequate.

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% for the single parameter 'vendor_slug', which already lists valid values. The description adds no additional meaning beyond the schema, 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?

The description clearly states the tool retrieves 'seven tracked commercial commitments' for a frontier AI vendor, listing them explicitly. This differentiates it from sibling tools like 'search_corpus' or 'get_framework_version_history' by focusing on commitments.

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 does not provide explicit when-to-use or when-not-to-use guidance. However, the purpose is clear enough that an agent can infer usage context from the sibling tool names.

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 must carry the behavioral transparency burden. It explains that the tool returns matching provisions with URLs and confidence tags, but does not mention result ordering, idempotency, rate limits, or authentication needs. The lack of destructive behavior is implied but not explicit.

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 long, front-loading the purpose and scope. Every sentence is meaningful and there is no redundant or extraneous 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 search tool with 4 parameters and no output schema, the description covers the key aspects: what is searched, return values (URLs, confidence tags), and parameter context. It could mention result sorting or pagination behavior, but it is largely complete.

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%, with all parameters having descriptions. The description adds value beyond the schema by explaining how the query matches (label, summary, excerpt) and providing examples for jurisdiction. It also connects the 'legal lenses' concept to the lens parameter.

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 searches a corpus of AI-relevant regulatory provisions across jurisdictions and legal lenses, and specifies return values. It distinguishes from sibling tools which are about framework version history, partnerships, and commitments.

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 provides clear context for when to use (searching AI regulatory provisions) but does not explicitly state when not to use or list alternatives. The sibling tools are sufficiently different to imply usage scope.

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