site

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

With no annotations, the description carries the full behavioral burden, but it only names the subject matter. It doesn't disclose whether the tool is a read-only query, how results are returned, or any side effects or constraints.

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 text is short and front-loaded, but it is a noun phrase rather than a complete sentence with an action verb. The brevity is not fully backed by structural clarity.

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 zero-parameter tool with no output schema and no annotations, the description gives the essential subject area but omits the expected action/return type. An agent could infer the purpose from the tool name and title, but the description alone leaves gaps.

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 input schema has zero parameters, so there are no parameter semantics for the description to clarify. A baseline of 4 is appropriate because no parameter documentation burden exists.

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 identifies the resource (criteria and filters of the US virtual data room providers comparison) but lacks a verb that states what the tool does with them. It is more specific than the title, yet it doesn't say whether it returns, lists, or explains these criteria, and it doesn't clearly differentiate from compare_options or compare_table.

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 on when to invoke this tool versus any sibling tool. It provides domain context (US virtual data room providers comparison) but no exclusions, prerequisites, or alternative routing.

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 of behavioral disclosure. It conveys that the operation returns options and can be filtered by tag, which is useful. However, it does not explicitly state that the operation is read-only, what the filter behavior is when no parameters are provided, or any limits on the returned 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 a single sentence with no filler and front-loads the main subject. The parenthetical list of fields is somewhat dense, but overall the length is appropriate for a simple tool.

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 two-parameter list tool, the description gives the basic shape of the data and the filtering option. However, the filter parameters themselves remain ambiguous, there is no output schema to clarify the return format, and no guidance on when this tool is preferable to its siblings. More context is needed for fully 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?

The input schema has zero description coverage, and the tool description only mentions filtering by a tag value. It does not explain what filterKey means, what values are valid, or how filterKey and filterValue interact. The description must compensate for the bare schema, but it only partially does so.

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 resource (options in a US VDR comparison) and the included fields (name, best-for, tags), which helps an agent understand what it will get. However, it lacks an explicit verb like 'list' or 'get,' and the noun-phrase structure is slightly awkward. It is still distinguishable from siblings like compare_criteria and compare_table.

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 the sibling tools, such as compare_criteria or compare_table, and no exclusions are stated. The only usage hint is the optional tag filtering, which is not enough to route an agent to the correct tool among the 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?

There are no annotations, so the description carries the behavioral disclosure burden. It does convey that the tool returns a full table of values for all options and criteria, which is informative, but it does not describe any filtering behavior beyond what the schema already says, nor potential limitations such as missing values, display order, or data source caveats.

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, focused sentence that front-loads the core behavior: returning each option's value for every criterion. It is economical with no filler, though it sacrifices some explanatory context about alternatives or edge cases.

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 optional parameter and no output schema, the description provides enough conceptual context to understand the return value: a table of options versus criteria. Combined with the inline schema for the parameter, an agent can invoke it correctly, although a note distinguishing it from sibling tools would make it more 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 100%, and the parameter is already documented as 'one option's name, else all'. The description's use of 'option's value' loosely aligns with the parameter but adds no new semantic detail beyond the schema, so the baseline score of 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 returns a full comparison table mapping options to criterion values, using a specific resource ('the US virtual data room providers comparison'). It implicitly distinguishes itself from sibling tools like compare_criteria and compare_options by emphasizing 'every criterion' and 'full table', though it could be more explicit about its exact action verb.

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 guidance is given about when to use this tool versus compare_criteria or compare_options. The phrase 'the full table' hints that this is the comprehensive view, but there is no clear statement of alternatives or exclusionary conditions, leaving the agent to infer usage 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 provided, the description carries the behavioral disclosure burden. It explains that the tool returns explanatory content about the enquiry process, including recipient information, consent wording, and confirmation steps. It also clarifies that the action described is free and non-binding, but it does not explicitly state whether this tool itself has no side effects.

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 reasonably concise and front-loaded with the 'Read first' instruction. Each sentence adds value, though some content in the title (not a purchase, not a guaranteed quote) is repeated in the description.

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 zero-parameter informational tool with no output schema, the description adequately explains what the tool returns and clarifies the scope of the underlying enquiry action. It could be more complete by explicitly saying when to reach for this tool rather than compare or enquiry_fields, but the essential context is present.

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 and the schema already covers this with 100% coverage, so the description does not need to explain parameter meanings. The baseline of 4 applies because the schema fully documents the empty parameter set.

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 that the tool 'States plainly what submit_enquiry does' and that it returns who receives details, consent wording, and confirmation method. This distinguishes it from actual submission tools like submit_enquiry, though it does not explicitly differentiate itself from compare_* or enquiry_fields siblings.

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 opening 'Read first' provides an explicit usage cue that this should be used before engaging with the enquiry flow. It clarifies what the enquiry does not involve (purchase, payment, guaranteed quote), but it does not explicitly mention alternatives or state when not to use this tool relative to 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 the behavioral burden. It discloses the full set of returned information and gives downstream usage guidance. It does not explicitly state that the operation is read-only, but a zero-parameter field-listing tool is inherently non-mutating and the description is otherwise 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?

The description is a single, information-dense sentence followed by one actionable cross-reference. Every part earns its place, and the most important content is front-loaded.

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 zero-parameter tool with no output schema, the description sufficiently explains what the tool returns and why it matters for submitting an enquiry. It could be more explicit about the exact response shape or how it differs from enquiry_describe, but these are not critical gaps.

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 zero parameters, the description is not required to explain parameter behavior. It adds useful context by explaining that the returned field keys are the keys to use in submit_enquiry, which helps the agent make the right connection.

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 resource (the Data Room Picker enquiry) and enumerates exactly what is returned: key, label, type, required flag, help text, and allowed options. It lacks an explicit verb like 'list' or 'returns' and does not directly distinguish from sibling enquiry_describe, but the intent is 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 tells the agent to pass answers to submit_enquiry keyed by field key, which implies this tool is the source of field keys for submission. However, it does not explicitly state when to use this tool versus the sibling tools, such as enquiry_describe.

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 behavioral burden. It discloses the stateful two-step flow, the required consent wording, the email with a click link, and the fact that providers only see the enquiry after the person clicks the link.

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 dense but well structured: purpose first, then step 1, then step 2. Every sentence earns its place, including the consent quote, which is essential for correct invocation.

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 complex two-step submission flow and no output schema, the description fully explains what is returned (summary, consent line, confirmation token) and what happens after submission (email, link click, provider visibility). Nothing critical is missing.

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?

Although schema coverage is 100%, the description adds important meaning by linking answers to enquiry_fields keys, requiring consent=true, and defining confirmation as the token returned from step 1 after the person approves the summary. This goes well beyond the bare 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?

The description states a specific action ('Submits an enquiry') and resource ('Data Room Picker'), immediately distinguishing it from a purchase or quote. It also names the two-step nature of the operation, which sets it apart from the sibling comparison and description tools.

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 gives explicit step-by-step call conditions: call first with answers and consent=true, show the summary, then call again only if the person agrees, using the confirmation token. It also clearly states what this tool is not for: not a purchase and not a guaranteed quote.

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