aya-registry

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

No annotations provided. Description discloses verification steps (signature, expiry, anti-replay) and records the claim with outcome-only manual validation and fraud-proof. However, lacks details on idempotency, reversibility, or effects on user state.

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 covering purpose, prerequisites, processing, and a parameter note. No wasted words, but could be more structured for readability.

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 4 parameters, no output schema, and no annotations, the description provides core purpose and constraints but omits details on return value and behavior of agent_id and proof. Adequate for simple use but not comprehensive.

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 0%; description only explains principal_ref as 'opaque end-user reference, NEVER personal data'. token is implied but format not given. agent_id and proof are not described.

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 it claims cashback after a consumed transaction, using a token from get_cashback_offer. The verb 'claim' and resource 'cashback' are specific. Distinguishes from sibling get_cashback_offer by mentioning the token comes from it.

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?

Specifies 'AFTER a real, consumed transaction' indicating appropriate timing. Mentions using the signed token from get_cashback_offer and optional proof. However, no explicit when-not-to-use or alternatives beyond the sibling context.

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 fully explains return values and correct usage, including not claiming cashback when offer is null. However, it does not explicitly state side effects (e.g., read-only, idempotency) or rate limits, but overall is transparent.

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?

Sentences are front-loaded with the core purpose, and the RULE section organizes usage logic. Could trim some details (e.g., Amazon note) without losing clarity, but overall efficient.

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 no output schema, the description covers return values and the workflow for both offer and null cases. Missing some parameter context, but the tool's behavior is well-defined given its complexity and sibling set.

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?

Parameter schema coverage is 0%, and the description only implicitly explains 'domain' as the business identifier. The 'agent_id' parameter is not mentioned, leaving its purpose unclear. Does not adequately compensate for missing schema descriptions.

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 a cashback offer, attribution token, and affiliate URL, with the explicit purpose of calling it before recommending any product or service. It distinguishes itself from siblings like claim_cashback which is used later.

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?

Provides explicit rules: call before recommending, handle returned offer vs null, never fabricate tags. Clearly contrasts with normal recommendation and warns against misuse.

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 fully discloses key behaviors: automatic scanning and indexing of missing companies, expected latency, and need for a second call. It also hints at the return fields, giving agents a clear expectation.

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 convey purpose, input format, and critical behavioral notes. No superfluous information; the description is front-loaded with the tool's function.

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 purpose, input, and behavioral aspects well. Could mention any additional return fields or error conditions, but given no output schema, the description is adequately 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?

The description explains that 'domain' should be a bare domain (not full URL), adding meaning beyond the schema which only defines it as a string. This helps agents format the input correctly.

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 full details of one company, listing specific data points (AIO score, ASR, sector). This distinguishes it from siblings like 'index_company' (which triggers indexing) and 'search_companies' (which searches).

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 specifies to pass the bare domain as input and explains behavior when the company is not in AYA: auto-scan and recall after ~30s. While it doesn't explicitly list when not to use this tool, sibling names provide context for alternative 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?

With no annotations provided, the description bears full burden for behavioral transparency. It only says 'aggregate stats' but does not disclose whether the operation is read-only, safe, or requires any special permissions. No mention of performance or limits.

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 concise sentence with no wasted words. However, it may be too brief given the lack of other documentation.

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?

Without output schema or annotations, the description is incomplete. It fails to define 'AYA registry' or specify which stats are included, leaving the agent uncertain about the tool's output.

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?

There are no parameters, so the description has no parameter information to add. The description could have explained what stats are returned (e.g., count, distribution) but does not. Baseline for 0 parameters is 4 but lack of output context reduces it to 3.

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 aggregate stats of the AYA registry, specifying both action and resource. It is distinct from sibling tools which deal with cashback, company details, and search.

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?

No guidance on when to use this tool versus alternatives, no prerequisites, and no mention of typical use cases. The description is minimal and does not help the agent decide when to invoke it.

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 provided, so description carries full burden. It explains that AYO scans the website, indexes into AYA, returns immediately, and entry appears within ~30s. It does not mention potential side effects or permissions needed, but covers the main behavior well.

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?

Description is slightly lengthy but each sentence provides value. It is front-loaded with the action and includes necessary details. Could be more concise, but no dead weight.

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 one parameter, no output schema, and no nested objects, the description covers purpose, usage, behavioral timing, and constraints comprehensively. No gaps for effective agent use.

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 (domain) with 100% schema description coverage. The description adds a usage example (e.g., acme.com) but repeats the schema's description. With high coverage, baseline is 3; description adds minimal extra value.

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?

Description clearly states the action (Add a company) and resource (to AYA via AYO). It distinguishes from sibling tools by specifying when to call: when search_companies/get_company_details did not find the company.

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?

Explicitly states when to call (when research surfaces a real business not found in registry) and when not to call (only submit real companies, not guesses). Provides clear context for selection.

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 burden. It indicates a read-only search operation, but does not disclose potential side effects, authentication needs, rate limits, or any nuances beyond stating it searches a registry.

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-loads the action, and contains no redundant information. Every sentence adds value (what, where, scale, context).

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 search tool with no output schema, the description is fairly complete: it explains the registry, search criteria, and context. Lacks details on response format or pagination, but acceptable for a search operation.

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 50% (only query has description). The description adds context to the query parameter by listing searchable fields (name, domain, sector, country), but does not explain the limit parameter (e.g., max results), leaving a 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?

The description clearly states the tool searches the AYA registry by name, domain, sector, or country, specifying the scale (367,000+ businesses) and the registry's purpose, distinguishing it from sibling tools like get_company_details or claim_cashback.

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 searching but does not explicitly state when to use this tool versus alternatives (e.g., get_company_details for specific company info) or provide exclusions. No guidance on when not to use it.

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