LeadProof Sales Agent

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

With no annotations provided, the description carries the full burden of transparency. It discloses that the tool is free and read-only (audit), and lists the outputs: a risk score, missing safeguards, revenue-at-risk estimate, integration kit, and checkout path. This is detailed, but lacks mention of any side effects or authorization requirements.

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 action and then lists key outputs. Every word adds value, with no redundancy or filler. It is appropriately scoped for a simple audit 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 complexity of 8 parameters (2 required), no output schema, and no annotations, the description should at least touch on the inputs. It does not explain what arguments are needed or their roles, leaving a significant gap in the agent's understanding of how to invoke the tool 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?

The input schema has 8 parameters with 0% description coverage, and the tool description does not explain any of them. The description focuses entirely on outputs, providing no meaning or constraint for inputs like 'stack', 'idempotency', or 'retry_policy'. The agent would have to infer parameter purpose from names 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 clearly states the tool performs a free reliability audit of a lead-delivery workflow, using a specific verb ('Run') and resource ('audit of a lead-delivery workflow'). The title 'Audit Lead Workflow' aligns, and it is distinct from sibling tools which focus on comparison, evaluation, or checkout.

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 auditing reliability and obtaining a risk score, but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites. There is no guidance on when not to use it.

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

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

Without annotations, the description carries the full burden. It describes the output contents comprehensively but does not state that the tool is read-only, whether authentication is needed, or any potential side effects. For a no-parameter tool, this is adequate but not exemplary.

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 purpose ('build-versus-buy comparison') and then lists covered aspects. While it is somewhat long, it is well-structured and every phrase adds value.

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 no output schema, the description sufficiently explains what the tool returns. No parameters means no missing parameter info. The tool is simple, and the description covers its essential functional scope. It could mention authentication or side effects, but overall it is complete enough for a read-like 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?

With zero parameters, schema coverage is trivially 100%. The description adds value by detailing the output, which is the only relevant semantic information beyond the empty schema. Baseline 4 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 'build-versus-buy comparison' and lists specific topics covered (retries, idempotency, pricing). This distinguishes it from siblings like 'evaluate_leadproof_fit' and 'get_leadproof_checkout', which have different purposes.

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 'evaluate_leadproof_fit' or 'get_leadproof_evidence'. The description implies a decision-making context but does not explicitly state prerequisites, when to avoid, or when another sibling is more appropriate.

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 bears full responsibility for behavioral disclosure. It only states the tool returns results but does not disclose side effects, idempotency, rate limits, or whether it triggers any external actions (e.g., creating records). This is insufficient for a tool returning multiple actionable items.

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, well-formed sentence of 25 words. It is front-loaded with the key action and output, with no extraneous 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?

Given the tool has 4 parameters, no output schema, and returns multiple items (ROI, plan, checkout URL), the description does not provide enough context about how inputs affect the evaluation or what the output format looks like. The agent may not know how to interpret the results.

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 low (25%). The description does not compensate by explaining the meaning of the three undocumented parameters (monthly_leads, workflow_problem, average_customer_value). It mentions ROI estimate and plan but does not connect them to inputs.

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 'Evaluate' and clearly states the object: whether a lead automation workflow benefits from LeadProof. It enumerates the returned items: integration path, ROI estimate, plan, checkout URL, which distinguishes it from siblings like audit_lead_workflow or get_leadproof_checkout.

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 initial evaluation but does not explicitly state when to use this tool versus siblings (e.g., for comparing with in-house or for a trial). No when-not-to-use guidance is 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?

The description adds an important behavioral requirement: the agent must obtain principal authorization before payment. No annotations exist, so this disclosure is valuable. However, other potential behaviors (e.g., idempotency, error handling) are omitted.

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 with no wasted words. The first sentence delivers the core purpose, and the second adds a critical usage constraint. Information is 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 tool with one parameter and no output schema, the description adequately covers purpose and a key behavioral requirement. It does not describe the return value format, but a URL is standard. Minor gap: no mention of error cases or idempotency.

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%, meaning the description provides no additional parameter information beyond what's in the schema. The enum values (builder, agency) are self-explanatory, but the description misses an opportunity to clarify their meaning.

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 'Return' and the resource 'official Stripe checkout URL for a LeadProof plan', making the tool's function unambiguous. It distinguishes itself from sibling tools by focusing on payment checkout.

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 like get_leadproof_trial or request_leadproof_handoff. The description mentions an authorization requirement, but lacks context for selection among siblings.

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 clearly states behavioral traits: accepts no input and returns no personal or customer data, implying a read-only, safe operation. It does not contradict annotations (none provided).

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 long sentence that front-loads the main purpose. It could be broken into two for clarity but is reasonably concise with no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and no output schema, the description lists all returned components, providing a complete picture. It does not mention error behavior or authorization, but these are less critical for a zero-input read 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 schema description coverage is 100%. The description adds that it accepts 'no input', which is already implied by the empty schema, so no extra meaning is needed.

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 'the official no-purchase evaluation sequence' and lists five specific components (buyer guide, fictional example audit, browser-only calculator, public workflow tests, no-card production trial). This distinguishes it from siblings like get_leadproof_trial or get_leadproof_evidence.

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 or when-not-to-use guidance is provided. The description implies the tool is for evaluation without purchase, but it does not contrast with sibling tools such as 'audit_lead_workflow' or 'get_leadproof_checkout'.

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 discloses what the tool returns (public manifest, live checks, etc.) and what it excludes (customer records). However, there are no annotations, and the description does not address authentication needs, rate limits, or whether the 'live checks' imply some side effect. It provides moderate transparency but lacks comprehensive behavioral context.

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 long, with the first sentence front-loading the core purpose. Every word adds value; there is no redundancy or filler. It is appropriately sized for a tool with no parameters.

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 lack of annotations and output schema, the description provides a good overview of the return content and an important exclusion (no customer records). It could be improved by noting whether the tool requires authentication or if it's publicly accessible, but it is largely complete for an agent to decide to invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so schema coverage is 100%. The description adds value beyond the schema by detailing the content of the returned data, which helps the agent understand what to expect without needing parameter explanations.

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 the public LeadProof evidence manifest and lists specific components (live checks, contracts, protections, limitations). It is a specific verb+resource that distinguishes from siblings like audit_lead_workflow or get_leadproof_trial.

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 explicit guidance on when to use this tool versus alternatives. It only mentions that it contains no customer records, which is a helpful exclusion, but no comparison with sibling tools or context about when to choose this over others.

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

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

With no annotations, the description carries full burden. It discloses that the tool reads 53 guides and returns specific outputs, and mentions 'no-card trial' as a behavioral nuance. However, it does not disclose whether the operation is read-only, any permissions, or error behavior. It adds some value but could be richer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with the primary action, no wasted words, and includes all key information in a compact 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?

For a single-parameter lookup tool with no output schema, the description explains the matching logic and output types. It could mention what 'canonical guide paths' refers to or provide an example, but overall it is sufficiently complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so a baseline of 3 is appropriate. The description adds minimal extra meaning, restating stack types but not providing format examples or additional semantic detail 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 action ('Match an automation stack'), the resource ('53 published LeadProof platform guides'), and the specific outputs ('strongest canonical guide paths, no-card trial, and concise implementation steps'). This distinguishes it from siblings by focusing on integration instruction matching.

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 a user has a stack and needs integration instructions, but it does not explicitly state when to use this tool over siblings like get_leadproof_trial or evaluate_leadproof_fit. No exclusions or 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool 'accepts no input' and 'returns no personal or customer data', which is a meaningful privacy guarantee. It also explains the decision's ordering logic, adding behavioral context beyond a generic 'get decision'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the main purpose, and every clause adds information (purpose, decision details, input/output constraints). 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?

For a zero-parameter, no-output-schema tool, the description covers the essential behavior and outcome. It even notes privacy implications. It could arguably mention the exact return format or how the decision is computed, but given simplicity, this 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?

With zero parameters, the baseline is 4. The description confirms 'accepts no input', which aligns with the empty schema. No additional parameter explanations are needed or provided.

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 'the official ordered LeadProof plan decision' and specifies the exact decision contents (trial-first, then Builder or Agency checkout after authorization). This specific verb+resource combination distinguishes it from sibling tools like get_leadproof_checkout or get_leadproof_trial.

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 explicit guidance on when to use this tool versus the many sibling tools. It states what the tool does but omits any mention of scenarios, prerequisites, or alternatives, leaving the agent without clear 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?

Since there are no annotations, the description carries the full burden of behavioral disclosure. It clearly states 'It accepts no input and never authorizes payment,' which reveals key safety and side-effect traits. Additionally, terms like 'public' and 'fictional example' signal that the content is non-sensitive and non-real. This is strong transparency, though it omits details like authentication or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exactly two sentences. The first sentence enumerates the four deliverables in a clear, scannable list. The second sentence adds crucial constraints without redundancy. Every word earns its place, and the most important information 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?

With no output schema and no annotations, the description must provide complete context. It names the deliverables and explicitly notes no-input and no-payment authorization, which covers the essential operational behavior. However, it does not elaborate on the format or structure of the returned items, but for a zero-input tool with simple outputs, this 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 coverage is 100% with an empty properties object. The description reinforces this with 'accepts no input.' For a parameterless tool, the baseline is 4, and the description adds no unnecessary parameter detail, so this 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 explicitly states the tool 'Return[s] the public replay procedure, fictional example audit, exact replay requirements, and an attributed no-card production trial.' The verb 'Return' combined with specific deliverables clearly defines the tool's purpose. The title 'Test Failed-Lead Replay Before Purchase' further distinguishes it from sibling tools by emphasizing the pre-purchase testing context.

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 explicitly state when to use this tool versus alternatives like get_leadproof_trial or get_leadproof_evaluation_path. The title implies usage for pre-purchase replay testing, but no direct comparisons or exclusions are provided. This gives implied usage context but lacks explicit 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?

In the absence of annotations, the description discloses that no payment is required, a verified sign-in is needed, and it is a read-only retrieval operation. It does not mention any side effects or rate limits, but the information is adequate for a non-destructive action.

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 conveys the tool's purpose and key features. Every word adds value.

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 no output schema, the description sufficiently explains what the tool returns (trial details: 25 leads, 14 days) and prerequisites (verified sign-in). It is complete for a parameter-less 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 input schema has zero parameters, so schema description coverage is 100%. No additional parameter semantics are needed, and the description is not lacking in this area.

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 the official no-card production trial, specifying what it includes (25 live lead deliveries for 14 days). It distinguishes itself from sibling tools like get_leadproof_checkout, which likely involves payment.

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 tool is for obtaining trial details without payment, suggesting it precedes checkout. However, it does not explicitly state when to use vs alternatives like get_leadproof_checkout or request_leadproof_handoff.

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 full burden. It discloses key behavior: it does not authorize payment and requires consent. However, it does not detail side effects, data creation, or other operational impacts.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loading the purpose and concisely covering requirements and return behavior. Every sentence is informative with no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters, low schema coverage, and no output schema, the description is incomplete. It omits documentation for optional parameters, no explanation of 'recommended plan', and no guidance on consent capture or error conditions.

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 low (38%). The description reiterates required parameters (name, email, consent_to_contact) but does not explain optional parameters like plan, stack, company, or monthly_leads, missing an opportunity to add value beyond 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 creates a consent-based sales handoff for a represented buyer, specifying the verb 'create', the resource 'sales handoff', and the context. It also distinguishes the tool from siblings by focusing on handoff creation and returning a plan/checkout.

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 a represented buyer has given consent but does not explicitly state when not to use or provide alternatives among siblings. It mentions requirements but lacks comparative guidance.

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