ai-vendor-data-trust-index

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

With no annotations, the description carries the disclosure burden. It warns 'PAID (x402)' upfront and details return content, which is valuable. However, it does not mention other behavioral traits such as rate limits, authentication needs, error handling, or whether it's read-only.

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 highly concise: two short sentences with the cost warning front-loaded. Every clause adds value, efficiently conveying both the paid nature and the scope of the returned data.

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 structure (one parameter, no output schema, no annotations), the description adequately covers return content and the cost caveat. It lacks details on vendor identifier format or error scenarios, but for a straightforward retrieval tool, it is sufficiently 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 schema only defines a 'vendor' string with no description, and the description adds minimal semantic value by saying 'for one vendor.' This clarifies the parameter's role but provides no format, example, or allowed values, leaving the agent to infer what string to use.

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 'full source-cited data-trust record for one vendor' and enumerates the content (every field, quote, source URL, confidence, date). This specific scope distinguishes it from sibling tools like get_ai_vendor_trust_headline (likely a summary) and get_ai_vendor_trust_compare (likely multiple vendors).

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 when to use this tool by emphasizing 'full' and 'for one vendor,' which signals it is for complete single-vendor lookups rather than headline or comparison variants. It does not explicitly name alternatives or exclusions, but the context is clear enough.

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 burden. It adds useful behavioral context: 'FREE' indicates cost, 'default trains_on_your_data' reveals default behavior, and 'with citations' discloses output characteristics. It does not explicitly state read-only, but 'compare' strongly suggests a non-mutating 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?

Single sentence with all necessary information front-loaded. 'FREE' is attention-grabbing but relevant. No 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?

For a simple tool with one optional parameter and no output schema, the description covers the core behavior, default, and output characteristic (citations). It is sufficiently complete 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?

Schema coverage is 100% (parameter 'field' has an example), but the description adds crucial semantics: the parameter is optional and defaults to 'trains_on_your_data', and it clarifies that only one field can be compared at a time. This goes beyond 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?

Clear verb 'Compare' with specific resource 'data-trust field' across 'all tracked vendors'. Differentiates from siblings like get_ai_vendor_trust by emphasizing cross-vendor comparison and citations.

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?

Implies when to use: when a cross-vendor comparison is needed. Does not explicitly name alternatives or exclusions, but the contrast with sibling tools is clear from the scope 'across all tracked vendors'.

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 burden. It adds context like 'FREE' and 'source-cited', implying a read-only lookup that returns a plain-language verdict with citations. However, it does not disclose exact return format, error behavior, or any side effects, leaving gaps.

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?

A single, tightly written sentence that delivers the essential purpose and scope. Every word contributes, including the 'FREE' qualifier and the parenthetical examples.

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 one-parameter tool with no output schema, the description adequately conveys what the tool returns (a sourced, plain-language verdict) and the subject matter (vendor data handling). It doesn't specify the return type explicitly, but the description is sufficient for the tool's simplicity.

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 fully documents the vendor parameter with an example ('openai-api', 'anthropic-api', 100% coverage). The description adds no parameter-specific detail, 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 states a clear verb+resource (get a verdict on AI vendor trust) and specifies the scope (data handling categories like trains-on-data, zero-retention, opt-out). It does not explicitly differentiate from sibling tools get_ai_vendor_trust or get_ai_vendor_trust_compare, which would warrant a 5.

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 when-to-use guidance is provided. The description does not mention alternatives like the full trust report or comparison tool, leaving the agent to infer usage from the name alone.

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