Agent Intelligence Platform

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

There are no annotations and no output schema, so the description carries the behavioral burden. 'Get' implies a read operation and 'machine-readable' gives a format hint, but there is no explicit statement about side effects, permissions, or response structure.

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?

One tightly written sentence, front-loaded with the action and resource, with no filler. The list of returned data categories is compact.

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 read tool, the description names the input and the broad output categories. However, with no output schema and no usage guidance, an agent still lacks details about the exact machine-readable format and when to prefer this 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?

With 0% schema description coverage, the description's 'a shelf's' weakly ties shelf_id to the resource being described. This is enough for a single obvious identifier, but it doesn't state the expected format or where to find the shelf_id.

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 action ('Get') and resource ('a shelf's ... status'), listing the returned categories. It is clear but does not explicitly distinguish this from sibling tools like query_intelligence or list_intelligence.

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 is given about when to use describe_intelligence versus list_intelligence, query_intelligence, or search_intelligence. The description implies a read for a shelf but does not state exclusions or 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?

No annotations exist, so the description bears the full burden of behavioral disclosure. It conveys that the tool lists shelves and includes statuses, but it does not mention read-only behavior, output shape, pagination, or the effect of the live_only parameter.

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, front-loaded sentence with no filler. It is concise, though it omits important parameter guidance, making it slightly underwhelming in terms of overall helpfulness.

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 tool with one parameter, no output schema, and no annotations, the description is incomplete. An agent cannot correctly use the optional parameter or distinguish this tool from its siblings based on the provided information.

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 0%, and the description does not mention the 'live_only' parameter at all. There is no information anywhere explaining what the parameter means or how to use it.

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 identifies the action ('List'), the resource ('AIP shelves'), and the included details ('access and publication status'). It is specific enough to distinguish the broad listing intent from the other intelligence siblings, though it does not explicitly contrast them.

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?

There is no guidance about when to use this tool versus describe_intelligence, query_intelligence, or search_intelligence. The verb 'List all' implies an enumeration use case, but no explicit context or exclusions are provided.

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 discloses the key behavioral trait: it returns either a zero-result answer or x402 payment instructions. However, it doesn't explain what x402 means, what happens after payment, or whether this is a read-only operation. The description adds meaningful behavioral context but leaves 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?

Two sentences, front-loaded with the core purpose and outcome. Every word earns its place, and the x402 payment instruction is a compact way to convey the tool's unique behavior.

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 tool with no output schema and no annotations, the description covers the core behavior but omits important context: what x402 means, how params affect the query, and what a 'zero-result answer' looks like. It's adequate for a simple probe tool but not fully 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 description coverage is 0%, so the description must compensate. It mentions 'shelf' and 'matching paid intelligence' but doesn't explain the 'params' object at all. The description gives no detail on how shelf_id or params should be structured, leaving the agent to infer from 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 uses a specific verb ('Probe') and resource ('a shelf'), and clearly distinguishes this from the sibling tools by framing it as a cost-free way to test whether paid intelligence exists. It doesn't explicitly name a sibling, but the contrast with 'matching paid intelligence' implies the distinction from search_intelligence.

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: when you want to check a shelf without spending, and it hints at the alternative (paid intelligence retrieval). It doesn't explicitly say 'use search_intelligence instead when you want full results', but the zero-result vs x402 payment instruction contrast gives clear 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 carries the full burden for behavioral disclosure. It mentions a distinguishing behavior ('distinguishes live supply from demand tests') but does not clarify whether the operation is read-only, how results are filtered, whether experiments are included by default, or what the response contains. The one behavioral hint is too vague to rely on.

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 efficient sentence with the main verb front-loaded and no filler. It earns high marks for brevity, though the compressed wording makes some parts cryptic.

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?

The tool is simple (2 params, no nested objects), but there are no annotations or output schema, so the description must cover return behavior, filtering semantics, and the role of include_experiments. It does none of this, and central terms like 'AIP intelligence shelves' are left undefined. The context is insufficient for confident invocation.

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 0%, so the description must compensate for explaining parameters, but it does not. 'Matching a need' loosely implies the query parameter, but include_experiments is entirely unexplained and its effect on live supply versus demand tests is unclear. The description adds minimal meaning beyond the parameter names.

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 specific action ('Find') and resource ('AIP intelligence shelves'), and adds a distinguishing trait by noting it separates 'live supply from demand tests.' However, the domain terms are opaque and it does not explicitly differentiate from sibling query_intelligence, so it is clear but not fully distinguishing.

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 explicit when-to-use guidance or alternatives are named. The phrase 'matching a need' implies a search-oriented use compared to list/describe/query siblings, but the description never states when to prefer this tool over query_intelligence or what scenarios it excludes. This leaves the agent to infer usage rather than be told.

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