commercial

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

Annotations already indicate read-only and idempotent behavior. The description adds a valuable constraint: no access to customer projects or private data, which informs users about the tool's scope and privacy boundary. No contradiction with annotations.

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 short sentences: the first states the core purpose, the second adds an important limitation. No filler or redundancy; front-loaded and 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?

For a simple navigation/query tool, the description covers what it does, its public-data scope, and the private-data limitation. The lack of an output schema and explicit parameter guidance is a gap, but the core behavior is sufficiently described.

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 only implies that 'query' should contain the prospective need; it gives no detail about query formulation or the 'locale' parameter. With schema description coverage at 0%, the description does not sufficiently compensate for the missing parameter semantics.

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 action ('match') and resource ('a prospective need with public DigitalPublic capabilities, illustrative use cases and plans'), clearly distinguishing it from sibling tools that list, compare, or assess. The title reinforces the discovery/navigation role.

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 phrase 'prospective need' implies use during commercial discovery, and the note about no access to private data provides a boundary. However, it does not explicitly state when to prefer this over alternatives or name sibling tools for related tasks.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safe, non-mutating nature. The description adds no further behavioral context, such as how the plan is generated or whether it depends on live data. While it does not contradict annotations, it does not enrich the behavioral picture beyond the bare purpose.

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 that directly states the action and lists the input factors. Every word is purposeful, and there is no wasted or redundant 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?

The tool has 6 optional parameters, no output schema, and minimal descriptionization. The description fails to explain what the recommended plan looks like, how outputs are returned, or what the defaults imply. This incompleteness is notable given the absence of an output schema and the moderate complexity of the input 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?

The schema has 0% description coverage, but the description paraphrases most parameter names (e.g., 'project count', 'team size', 'live-data need'). However, it does not add true semantic meaning, such as units, constraints, or how these factors influence the recommendation. It also omits the 'locale' parameter entirely, leaving the agent with limited understanding of input semantics.

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 'Recommend' and names the resource 'a plan', clearly indicating the tool's function of recommending a plan based on several input factors. This distinguishes it from sibling tools that compare offers or estimate ROI, as it focuses on suitability recommendation.

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 any guidance on when to use this tool versus alternatives. It merely states what the tool does without specifying scenarios, prerequisites, or comparing to other tools like get_offer or compare_offers. This leaves the agent without explicit direction on 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?

Beyond annotations, it discloses the token is delivered exactly once, requires payment verification for paid plans, and explains the rotation fallback. These are critical behavioral traits not captured by annotations.

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 dense sentences, each adding value: action, one-time warning, and paid-plan/rotation caveat. No wasted words.

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 source tools, prerequisites, one-time delivery, output contents, and post-claim behavior. Missing error conditions and intentId meaning, but overall robust for a 2-param 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?

The description explains claimSecret as the one-time secret from start tools, but intentId is left undefined. With 0% schema coverage, this partial explanation leaves 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 redeems a one-time claimSecret to receive agent credentials, projectId, and MCP URL. It explicitly names start_sandbox and start_checkout as sources, distinguishing it from sibling 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?

It explicitly says to use after start_sandbox or start_checkout, and provides a when-not-to-use case: if already claimed, a human administrator must rotate credentials via UI. This gives clear contextual 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description does not need to restate safety. It adds valuable context about the scope and dimensions of the comparison (setup, governance, cross-channel work, tradeoffs) and emphasizes 'objective,' which clarifies expected behavior. No contradictions with annotations.

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 that front-loads the primary purpose ('Provide an objective comparison') and then lists the key comparison dimensions. No unnecessary words or repetition; it is perfectly concise.

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 comparison tool with no output schema, the description explains what is compared and the main axes of comparison, which covers the core use case. The main gap is the lack of detail on the output format or how the locale parameter affects results, but given the tool's simplicity and strong annotations, the description 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 fully defines the single parameter 'locale' with an enum and default, making it self-explanatory. However, the description provides no additional meaning or purpose for the parameter beyond what the schema already offers, and schema_description_coverage is 0%. Given the simplicity and clarity of the schema, a 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 identifies the tool's purpose: to provide an objective comparison between direct provider MCP servers and DigitalPublic, with specific comparison dimensions (setup, governance, cross-channel work, tradeoffs). This distinguishes it from sibling tools like compare_offers or assess_fit.

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 establishes a clear context for use (when a comparison between direct integrations and DigitalPublic is needed), but does not explicitly state when to use this tool over alternatives or provide exclusion criteria. It implies a decision-support scenario but leaves the agent to infer the exact differentiation from 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?

Annotations already declare readOnly and idempotent safety. The description adds that the comparison uses the canonical catalog, providing context about the data source. However, it does not disclose return format or other behavioral details.

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, clear sentence that front-loads the action and object, with no unnecessary words.

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 read-only comparison tool with a simple parameter, the description is adequate but lacks information about the output format or what the comparison results contain. The lack of an output schema increases the need for such details.

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 has one parameter (planIds) with 0% description coverage. The description clarifies that the parameter refers to plans and requires at least two, but does not explain the format or where plan IDs come from.

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's function: comparing two or more plans using the canonical catalog. It distinguishes itself from sibling tools like compare_integration_options by specifically targeting plans.

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 the tool is for comparing multiple plans, but does not explicitly state when to use it over alternatives or provide exclusions. The 'two or more' phrasing hints at use cases with multiple plans.

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?

Annotations already declare readOnlyHint and idempotentHint, so safety is covered. The description adds a small behavioral detail by stating the explanation is 'for humans and AI agents', which hints at output audience, but it doesn't disclose return format or other behaviors. This is consistent with annotations, adding minimal value.

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, front-loaded with the main action, and contains no filler. It is concise and easy to parse, earning full marks for efficiency.

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 and minimal description, the tool's return value is unspecified. It doesn't describe what the explanation will contain (e.g., capabilities, use cases, architecture), making it incomplete for an agent that needs to know what to expect. The low complexity partially mitigates this, but the lack of return details is a significant gap.

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 does not mention the 'locale' parameter at all. While the parameter is simple (enum es/en, default es), the description fails to explain its purpose or effect, leaving the agent to rely solely on the parameter name.

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 ('explain') and resource ('DigitalPublic'), clearly stating the tool's function. It distinguishes from sibling tools which are action-oriented (e.g., get offers, compare options) by focusing on a general platform overview, though it doesn't explicitly call out the differentiation.

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 use this tool versus alternatives. The description does not mention any context or exclusions, leaving the agent to infer that this is for introductory platform information, but without explicit direction.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's 'never forecasts' adds extra behavioral context beyond annotations. However, it doesn't detail return format or side-effect behavior beyond what annotations cover, but the added exclusion strengthens transparency.

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, zero redundancy, and the core purpose is front-loaded. Every word contributes to the definition and limitation.

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 description covers the tool's scope but omits output format and parameter semantics. With no output schema, it would benefit from stating what the result looks like (e.g., 'returns net value and break-even hours'). Also, planId enum meanings are not explained. It's adequate but not complete for a 4-param calculation 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?

Schema description coverage is 0%, and the description only loosely refers to 'entered time savings' and 'plan cost' without mapping to the actual parameters (hoursSavedPerMonth, hourlyCost, planId, currency). It does not explain enum values or parameter relationships, so it provides minimal value 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?

The description clearly states the tool calculates time savings value, plan cost, net value, and break-even hours. It also explicitly excludes sales/advertising forecasting, which differentiates it from any vague interpretation and from sibling tools that may assess commercial fit or capability.

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 phrase 'Calculate only' and 'It never forecasts sales or advertising performance' gives a clear boundary for when to use this tool (ROI from time savings) and when not to (sales/ad forecasting). It does not name specific sibling alternatives, but the exclusion is explicit, which is sufficient 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?

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is known. The description adds content scope (types of capabilities) but no additional behavioral traits such as return format, pagination, or error conditions. This matches the baseline for a read-only tool with annotations.

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 action and resource. Every word adds value, with no redundancy or filler.

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 adequately explains what is returned. It could be slightly more explicit about the 'explicit qualifications' phrase, but overall it is complete enough for an agent to invoke 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 description coverage is 0%, but there is only one optional parameter (locale) with a clear enum and default in the schema. The description does not mention or add meaning to this parameter, yet the schema alone makes it self-explanatory, so the description is not required to compensate heavily.

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 ('List') and identifies the resource ('capability matrix'), enumerating the types of capabilities included. This clearly distinguishes it from sibling tools, none of which mention capability matrices.

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 no guidance on when to use this tool versus the many sibling get/list tools. It does not mention contexts, alternatives, or exclusions, leaving the agent to infer applicability.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows it is a safe, repeatable read operation. The description adds the specific services being checked, which is useful context, but it does not disclose any additional behavioral traits such as data freshness, permission requirements, or response structure beyond what is already implied.

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, concise sentence that is front-loaded with the action ('Report whether') and immediately states the three readiness areas. There is zero redundancy or filler.

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, parameterless, read-only status check, the description fully captures the tool's purpose and output (availability of three specific onboarding paths). The output schema is absent, but the description's implied result (booleans for each service) is sufficient for this simple 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?

The tool has zero parameters, and the schema coverage is trivially 100%. The description does not need to explain parameters. It clearly states what the tool reports, which is sufficient for a no-parameter tool.

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 the specific verb 'Report' and clearly states the resource ('checkout readiness') with the exact scope: Sandbox, self-service checkout, and Enterprise proposal onboarding. This distinguishes it from sibling tools like dp_commercial_get_checkout_status, which likely reports on a specific checkout session rather than overall readiness.

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 on when to use this tool versus alternatives. It does not name any sibling tools or provide context such as 'use before starting checkout'. The description only states what it does, leaving usage implicit from the tool name and the listed readiness areas.

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?

Annotations already declare readOnlyHint and idempotentHint, so the description adds meaningful context beyond that: it specifies the response is 'non-sensitive', lists all possible nextAction values, and explains the credentialClaimed field. This gives the agent a clear behavioral model of the output without contradicting the annotations.

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 with no fluff. The first sentence states the core action, and the second packs essential details about output values into a clear, enumerative structure. It is well-organized and 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?

Given the tool's simplicity (one parameter, no output schema), the description covers key behavioral details: return type (non-sensitive status), the full set of nextAction values, and the credentialClaimed flag. The only missing piece is explicitly linking intentId to the creation tools (e.g., dp_commercial_start_checkout), but the description still provides enough context for straightforward usage.

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 explicitly explain the intentId parameter. It implies intentId identifies the request by mentioning 'a Sandbox, checkout, or proposal request', but it never states how to obtain intentId or its format. For a required parameter, this is a notable gap, though the single parameter is somewhat self-evident from context.

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 ('Return') and names the resource (status of a Sandbox, checkout, or proposal request). It clearly distinguishes this from the many sibling tools by focusing on status retrieval and even enumerating expected values, making its purpose unmistakable.

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 practical usage context by explaining the meaning of nextAction values and explicitly pointing to dp_commercial_claim_credentials as the follow-up action for AGENT_CLAIM_CREDENTIALS. It does not explicitly state 'use this when checking status' or contrast with similar tools like dp_commercial_get_checkout_readiness, but the context is sufficient for a single-purpose tool.

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?

Annotations already declare readOnlyHint and idempotentHint, so safety is covered. The description adds contextual detail about the returned content (verified legal identity, corporate contacts, evidence URLs), going slightly beyond what annotations provide. It does not reveal localization nuances, but the read-only nature is already established.

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 that immediately states the tool's purpose and key outputs. No filler or redundancy, making it easy to parse and 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?

Despite lacking an output schema, the description lists the main return elements, giving a clear expectation of the response. However, it omits the role of the 'locale' parameter, which limits completeness for a tool with optional localization. Overall, the low complexity makes the description mostly sufficient.

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 documents the 'locale' parameter with enum values and a default, but the description provides no explanation of how 'locale' affects the output. With schema_description_coverage at 0%, the description was expected to compensate, yet it omits any reference to the parameter, leaving its semantics ambiguous.

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 the specific verb 'Return' and names the exact resources: 'verified legal identity, corporate contacts, and evidence URLs for DigitalPublic.' This clearly distinguishes the tool from siblings like dp_commercial_get_offer or dp_commercial_get_security_profile, which target different data.

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 its use case through the tool name and content, but it does not explicitly state when to use it over alternatives or provide exclusions. It lacks comparative guidance such as 'use this instead of get_offer for legal entity details,' so the guidance is only implicit.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description doesn't need to restate safety. It adds value by disclosing the specific content areas (authentication modes, scopes, human prerequisites, review state, known limitations). This goes beyond annotations by clarifying what information is exposed, though it doesn't mention any potential side-effects or additional 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?

The description is a single sentence that is front-loaded with the main purpose, concise, and contains no filler. Every word contributes meaning, covering both the scope and content of the 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 one-parameter, read-only informational tool with strong annotations, the description fully covers what is needed. It explains the key aspects of what the tool returns without requiring an output schema, and it is clear enough for an agent to select and invoke 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 0%, so the description must compensate. It indicates the connectorKey refers to a specific DigitalPublic connector ('for one DigitalPublic connector'), but it does not explicitly define the parameter's format, where to find it, or any constraints beyond the schema's minLength. Since there is only one obvious parameter, this partial compensation is adequate but not fully explicit.

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 'Explain' and clearly identifies the resource: 'connection requirements for one DigitalPublic connector'. It enumerates the exact aspects covered (authentication modes, scopes, human prerequisites, review state, known limitations), which distinguishes it from sibling tools like get_security_profile or get_data_processing_profile.

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 when one needs connection requirements for a specific connector, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions. There is no mention of a sibling tool like get_security_profile that might overlap, so guidance is only implied.

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?

Annotations already mark the tool as read-only and idempotent, so the description's 'Explain' is consistent. It adds useful context beyond annotations by specifying the exact topics covered, including references to subprocessor evidence URLs, giving the agent a clearer picture of the tool's informational output without risking contradiction.

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 front-loads the main verb and resource, followed by a compact list of specific topics. It contains no filler or redundant wording, earning every character.

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 read-only informational tool with no output schema, the description gives a thorough list of content areas and hints at return values (evidence URLs). It could be slightly more explicit about the output format, but the level of detail is sufficient for the agent to understand what it will receive.

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%, but the single `locale` parameter is well-defined by its enum (es/en) and default value, making its purpose self-evident. The description does not mention the parameter, but this is a minor omission given the schema's clarity, so a baseline score 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 uses a specific verb 'Explain' and resource 'data processing profile', then details distinct content areas (controller/processor roles, AI training policy, DPA, subprocessors) that clearly differentiate it from sibling tools like get_security_profile or get_company_profile. The resource and topic list make the tool's purpose 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?

No guidance is provided on when to use this tool versus alternatives such as get_security_profile or get_company_profile. The description only lists what it explains, leaving the agent to infer applicability without explicit exclusions or comparisons.

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?

The annotations already declare readOnlyHint and idempotentHint, so the description's addition of 'current' provides a small behavioral nuance (that plans may not all be current). However, it does not elaborate on return format, error behavior, or other operational details, so it remains at a baseline level.

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 redundancy or filler, making it very concise and easy to parse.

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 get-by-id tool with one parameter and no output schema, the description sufficiently conveys the core action. It doesn't need to explain return values since no output schema exists, and the tool's simplicity reduces the need for extensive context.

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 has 0% description coverage for planId, and the description only says 'by id', which minimally clarifies that the parameter is an identifier. It does not explain what a planId looks like, its format, or its meaning beyond being an ID, leaving significant semantic 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 'Return one current plan by id' clearly states the verb (return), resource (plan), and scope (one current plan by id), which distinguishes it from sibling tools like list_offers (all plans) and compare_offers (comparison).

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 when you have a specific plan id, but it does not explicitly state when to use this tool versus alternatives such as list_offers or compare_offers. No exclusions or prerequisites are mentioned.

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?

Annotations already declare readOnlyHint and idempotentHint, and the description adds that the report is synthetic and contains no customer data, which is valuable behavioral context. It also aligns with the annotations, with no contradiction.

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 of eight words, front-loaded with the action ('Return') and devoid of filler. It is maximally concise.

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 (zero parameters, read-only, idempotent), the description sufficiently conveys the purpose and key characteristic (synthetic, no customer data). However, it does not specify the structure or format of the sample report, which could be important for an agent expecting a particular output type, though no output schema is provided.

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 accepts no parameters, so the schema is empty and the description correctly omits parameter details. The baseline of 4 applies.

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 synthetic report sample, using the verb 'return' and specifying the resource ('synthetic report sample') and its scope ('without customer data'). This distinguishes it from sibling tools like get_offer or get_company_profile, which deal with actual data.

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 the tool is for obtaining a sample report, but it does not explicitly state when to use it over alternatives or mention exclusions. There are no sibling tools with similar functionality, so the context is somewhat inherent, but the guidance is not explicit.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds substantive behavioral context by enumerating the topics the explanation covers (isolation, scopes, approvals, token revocation, audit controls), which sets expectations about the content returned. No contradictions with annotations.

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 clear sentence that front-loads the key content topics. Every word earns its place, and there is no redundancy or filler. It is appropriately sized for a simple no-parameter information 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?

Given the tool's low complexity, no parameters, and helpful annotations, the description is complete. It clearly specifies the scope of the explanation and needs no additional details about return values or behavior. The listed topics give the agent a precise understanding of what information this tool provides.

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, so parameter semantics are trivially satisfied. The baseline of 4 for a no-parameter tool applies, and there is nothing more the description could add beyond what is already evident from the empty input 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 clearly states the tool's purpose: it explains specific security-related topics (project isolation, scopes, approvals, token revocation, audit controls). The verb 'Explain' combined with the resource 'security profile' makes the function specific and distinct from sibling tools like get_company_profile or get_data_processing_profile.

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 about when to use the tool—when you need to understand the security profile aspects listed. It does not explicitly state when not to use it or name alternatives, but the topic list and tool name make the use case apparent. The absence of alternatives is acceptable given the tool's unique focus.

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?

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, but the description adds crucial behavioral caveats: the independent monitor source, the meaning of 'unknown' (never assume healthy), and the absence of uptime history or SLA. These details go well beyond the annotations and prevent misinterpretation.

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 four sentences, each serving a distinct purpose: stating the result, explaining measurement origin, clarifying the 'unknown' semantics, and setting boundary expectations. It is front-loaded with the main purpose and contains no redundant or filler content.

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, zero-parameter status tool with no output schema, the description covers all necessary context: what it returns, how it measures, critical interpretation caveats, and limitations. This is complete for an agent to select and invoke the tool correctly without further assumptions.

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, so the input schema is empty. According to the rubric, a zero-parameter tool receives a baseline of 4. The description does not need to explain parameters, and it appropriately focuses on behavior and output semantics.

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's purpose: returning current component health and timestamp. It distinguishes from historical tools by explicitly noting it does not invent uptime history or an SLA, setting it apart from siblings like dp_commercial_get_uptime_history.

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 current status checks and explicitly excludes historical data or SLA information, providing clear context for when to use this tool. However, it does not name an alternative tool, so it falls short of an explicit 'when-not' guideline.

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?

Annotations already mark readOnlyHint and idempotentHint. The description adds behavior beyond that: always returns coverage, returns 'insufficient_data' for low coverage, and discloses no contractual SLA. This is helpful context for interpreting results.

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?

Four sentences, front-loaded with the main purpose. The explanatory sentence about coverage is informative but slightly verbose; still, every sentence adds 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 read-only tool with two parameters and no output schema, the description covers purpose, a key edge case, and SLA context. It is mostly complete, though parameter semantics are absent (but schema handles them).

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?

Input schema has 2 parameters with 0% description coverage. The description does not explain 'window' or 'componentId', relying entirely on the schema's enum names and default. Since the description adds no meaning beyond field names/types, it fails to compensate for the low coverage.

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 opens with 'Return historical uptime for a component', a specific verb and resource. It further distinguishes itself from siblings by emphasizing that uptime always comes with coverage, clarifying the tool's unique scope.

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 context about coverage and SLA but does not explicitly state when to use this tool versus sibling tools like dp_commercial_get_service_status. No alternatives are mentioned.

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?

Annotations already declare readOnlyHint and idempotentHint, so safety profile is covered. The description adds the 'commercial V1' scope but provides no extra behavioral details such as data source, pagination, or response format. This is acceptable but not rich.

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: 'List integrations included in commercial V1.' Every word contributes to purpose and scope, with no filler or repetition.

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 (no parameters, read-only annotations, no output schema), the description is largely sufficient. It could mention what 'integrations' refers to or the response shape, but for a simple list operation it 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 tool has zero parameters, and the schema is empty with 100% coverage. The description does not need to explain parameters, and the baseline for no-parameter tools is 4.

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 'List' with a clear resource 'integrations' and scopes it to 'commercial V1'. This clearly distinguishes it from sibling tools like list_offers and list_use_cases.

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 no guidance on when to use this tool versus alternatives. It only states what it lists, without mentioning exclusions, prerequisites, or related tools that might be more appropriate for other queries.

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?

Annotations already declare readOnlyHint and idempotentHint, so the read-only nature is covered. The description adds value by detailing what the list will contain (prices in both currencies, limits, seats, modules, checkout mode), which provides useful behavioral context beyond the annotations. No contradictions.

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 that is efficient and contains no filler. Every phrase adds meaningful information about the tool's output.

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 list tool with one optional parameter and no output schema, the description conveys the essential return content (plans with prices, limits, seats, modules, checkout mode). It is moderately complete, though it omits any explanation of the locale parameter and possible output format, which would slightly improve 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 0%, and the description does not mention the 'locale' parameter at all. While the schema includes an enum and default, the agent is left without guidance on how locale affects results, and the description fails to compensate for the low schema coverage.

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 'List' and the resource 'current plans', enumerating specific attributes (EUR/USD prices, limits, seats, modules, checkout mode). This distinguishes it from sibling tools like get_offer (single offer) or compare_offers (comparison).

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 this is for viewing all current plans, but it does not explicitly mention when to use it versus get_offer or compare_offers. No exclusions or alternative recommendations are provided, so the context is clear but the guidance is implicit.

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?

Annotations already declare readOnlyHint and idempotentHint. The description adds valuable context that outputs must be clearly labeled as illustrative and not presented as customer results or performance claims, which prevents misinterpretation. This goes beyond the annotation information.

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, front-loaded with the verb 'List', and contains no extraneous information. It is concise and easy to scan.

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 list tool with strong annotations, the description covers the essential purpose and the key behavioral constraint. It does not describe the output format or locale parameter, but these are largely self-evident given the schema and the nature of the 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?

The only parameter (locale) is fully defined by the schema with an enum and default, but the description does not mention it. With 0% schema description coverage, the description could have explained the parameter, but its absence is acceptable given the simplicity and self-explanatory nature of the enum.

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 and resource: 'List clearly labeled illustrative use cases'. This distinguishes it from sibling tools like list_offers and list_integrations by specifically targeting use cases. The title reinforces the purpose.

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?

Usage is implied by the name and description: it's for listing illustrative use cases. However, no explicit guidance is given on when to use this tool versus alternatives like describe_platform or get_capability_matrix, and no alternative tools are mentioned.

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?

Annotations already declare readOnlyHint: false and destructiveHint: true, indicating this is a state-changing, potentially destructive operation. The description adds that no contract or charge is created, which is valuable, but it does not explain the email verification mechanism, other side effects, or the response behavior. With annotations present, the description adds some context but leaves significant unknowns.

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 short sentences with no filler. The first sentence front-loads the action and target, while the second adds a crucial clarification. Every clause contributes meaning, making it exemplary in conciseness and structure.

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 4 parameters, no output schema, and a rich set of sibling tools, the description is too sparse. It does not specify prerequisites (e.g., email verification), the outcome of the proposal request, or the role of idempotencyKey. The agent is left without essential context to confidently invoke the tool in the correct scenario.

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 parameter understanding. The only hint is 'email-verified', which partially explains the email parameter, but there is no explanation for idempotencyKey, locale, or companyName. The agent cannot understand the purpose or constraints of three out of four parameters from the 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 requests an 'email-verified Enterprise proposal' and distinguishes it from similar actions by clarifying it does not create a contract or charge. This separates it from sibling tools like dp_commercial_start_checkout, making its specific purpose 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 implies usage for non-binding enterprise proposals via the explicit 'does not create a contract or charge' clause, giving clear context for when to use this tool instead of a purchase/completion action. However, it does not explicitly name alternatives or state conditions when not to use it, so it lacks the full 'when/when-not' guidance found in top examples.

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?

The description goes well beyond annotations by disclosing human verification, payment authorization, the one-time claimSecret, the redemption flow via dp_commercial_claim_credentials, and the status nextAction. This provides rich behavioral context and actionable next steps for the agent.

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 sentences, the first stating purpose and the second outlining the essential workflow. Every sentence provides necessary information without redundancy or 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 complex checkout process with no output schema, the description covers the key response element (claimSecret) and the follow-up flow, which is valuable. However, it omits details about other possible response fields, error conditions, and parameter semantics, preventing a perfect score.

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 by explaining parameters. It does not mention any of the seven parameters (email, planId, market, currency, idempotencyKey, locale, companyName), leaving the agent without meaningful guidance on parameter usage. This is a critical 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 it creates an email-verified Sandbox, self-service checkout, or Enterprise proposal request, with a specific verb and resource. However, it does not differentiate from closely related sibling tools like dp_commercial_start_sandbox and dp_commercial_request_proposal, which limits it to a 4.

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 provided on when to use this tool versus alternatives. Given overlapping siblings (dp_commercial_start_sandbox, dp_commercial_request_proposal), the lack of any selection criteria or explicit usage context makes this a weak point.

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?

Beyond annotations, it reveals the one-time claimSecret, the need to redeem immediately, the 7-day email verification deadline, and suspension consequence. This is valuable behavioral context not present in annotations.

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 tightly written sentences with no redundancy; front-loads the purpose and then provides essential flow details.

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 description covers the enrollment workflow, the response artifact (claimSecret), and the critical 7-day verification constraint. Minor gaps on parameter specifics and sandbox meaning, but overall adequate for a starting 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 coverage, the description fails to explain the email, idempotencyKey, locale, and companyName parameters. It only indirectly references email for verification, not its role as a required enrollment identifier.

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 starts enrollment for an autonomous, synthetic, read-only sandbox, and references the claim flow, distinguishing it from sibling tools like dp_commercial_claim_credentials.

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 explicitly instructs to call dp_commercial_claim_credentials next, establishing the sequential workflow. It also mentions email verification deferred, which helps the agent understand the context. However, it doesn't explicitly specify when not to use or compare with start_checkout.

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