Generate realistic relational test data — 156 field types, 22 locales, JSON/CSV/SQL, free previews.
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Average 4.2/5 across 9 of 9 tools scored. Lowest: 3.2/5.
Each tool has a clear, distinct purpose. generate_test_data and generate_from_template could overlap, but descriptions distinguish custom/plain-English generation from pre-built templates. The payment lifecycle tools (create_agent_checkout, check_agent_checkout_status, claim_agent_api_key) are sequential and clearly scoped.
All tool names follow a consistent snake_case verb_noun pattern (e.g., create_agent_checkout, list_field_types, generate_test_data). generate_from_template uses verb_preposition_noun but remains in the same style. No mixing of conventions.
9 tools is well-scoped for a test data generation service with monetization. Each tool earns its place, covering data generation, schema discovery, and payment flow without redundancy or bloat.
The tool set covers the core generation workflows, schema construction, and the full checkout-to-key-claim lifecycle. Minor gaps exist, such as no API key management (e.g., revoke or refresh) and no template customization, but these are not critical for the primary use case.
check_agent_checkout_statusAPoll a Polar checkout created by create_agent_checkout using the returned claim_token.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes | claim_token returned by create_agent_checkout. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the dependency on create_agent_checkout and the polling nature, but does not disclose details like terminal statuses or retry behavior.
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 one sentence that efficiently communicates the tool's purpose and the required input. 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?
The tool is simple with one parameter and an output schema. The description provides sufficient context for a read-only polling operation, though it could arguably mention when polling should stop.
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 covers the single 'token' parameter 100%, and the description's 'using the returned claim_token' adds no new semantic information beyond what the schema already states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Poll') acting on a distinct resource ('a Polar checkout') and clearly ties it to a sibling tool ('created by create_agent_checkout'). It distinguishes from siblings like create_agent_checkout and claim_agent_api_key.
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 should be used after create_agent_checkout with the returned claim_token, giving clear context for when to call it. However, it does not explicitly exclude other tools or mention 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.
claim_agent_api_keyAInspectClaim the MockHero API key after Polar marks the loginless agent checkout as paid. The key is returned once.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes | claim_token returned by create_agent_checkout. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no safety hints (all false), so the description carries the burden. It discloses a key behavioral trait: 'The key is returned once', which indicates the operation is not idempotent and that repeated calls may not succeed. It also implies a state dependency (must be paid). This adds valuable context beyond the annotations, though it doesn't cover error behavior or permissions.
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, no filler. The first sentence states the action and prerequisite; the second adds a critical usage warning. Information is front-loaded and every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an output schema (which explains return value), the description covers the claim action, the prerequisite state, and the one-time nature. It integrates well with sibling tools in the checkout workflow. No obvious gaps for an agent to misuse the 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 coverage is 100%: the only parameter 'token' is described as 'claim_token returned by create_agent_checkout.' The tool description itself adds no parameter-specific detail, but the schema already fully explains it. Baseline 3 is appropriate as the description doesn't need to compensate.
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 clearly states the specific action ('Claim the MockHero API key') and the condition for use ('after Polar marks the loginless agent checkout as paid'). It distinguishes from siblings like create_agent_checkout (which creates) and check_agent_checkout_status (which checks status) by focusing on the final claim step.
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?
Explicitly states when to use ('after Polar marks the loginless agent checkout as paid'), which is a clear prerequisite. Also warns that 'the key is returned once', implying it should not be called repeatedly. However, it doesn't explicitly name alternatives or say when NOT to use, but the 'after' condition provides sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_agent_checkoutAInspectCreate a loginless Polar Checkout URL for MockHero's metered agent plan. Polar is the Merchant of Record for checkout, tax collection, and remittance. The card payment step needs a human: present the returned url to your operator together with the estimate_agent_usage cost, keep the claim_token private, then poll check_agent_checkout_status and call claim_agent_api_key once paid.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Billing email for the agent or the agent operator. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=false, openWorldHint=true), the description discloses critical behavioral details: Polar is the Merchant of Record, the card payment requires a human, the claim_token must be kept private, and the returned URL must be presented to an operator. These are significant operational facts not visible in annotations or schema.
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 concise and well-structured: the first sentence states the core purpose, and the second sentence provides essential workflow context. Every sentence adds value with no filler or redundancy.
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 key context: what the tool creates, how it fits into the larger flow (including estimate, status polling, and key claiming), the human-in-the-loop requirement, and the privacy of the claim_token. With an output schema present, the description is complete for this 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?
The schema already provides 100% coverage for the single 'email' parameter with format and description. The tool description does not add further parameter details, but the schema is sufficient, so a baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Create a loginless Polar Checkout URL for MockHero's metered agent plan.' It uses a specific verb and resource, and distinguishes it from siblings like check_agent_checkout_status and claim_agent_api_key by focusing on the creation step.
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 explicit workflow guidance: it tells the user to combine the returned URL with estimate_agent_usage cost, keep the claim_token private, poll check_agent_checkout_status, and call claim_agent_api_key after payment. This clearly indicates when to use this tool relative to alternatives and the recommended sequence.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
detect_schemaAConvert SQL CREATE TABLE statements or one sample JSON object into a MockHero schema that can be passed to generate_test_data.
| Name | Required | Description | Default |
|---|---|---|---|
| sql | No | SQL CREATE TABLE statement or statements. | |
| sample_json | No | Single example JSON object to infer fields from. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds the conversion behavior itself but does not disclose additional traits like error handling, limitations, or output details. With annotations covering safety, a 3 is appropriate.
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 conveys all essential information without redundancy. 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 the tool's moderate complexity, the read-only annotations, and the presence of an output schema, the description is sufficiently complete. It covers the two input modes and the purpose. It could mention the anyOf constraint explicitly, but the schema already handles that, so a slight deduction is reasonable.
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%: both parameters (sql and sample_json) are described in the input schema. The description mentions them but adds no extra detail beyond what the schema already provides, so the baseline of 3 is justified.
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 ('Convert') and clearly identifies the resources (SQL CREATE TABLE statements, sample JSON object) and the output (MockHero schema). It distinguishes itself from sibling tools like generate_test_data by noting the schema is 'passed to generate_test_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 when to use this tool: when you have SQL or a sample JSON object and need a schema for generate_test_data. It provides clear context but does not explicitly mention alternatives or exclusions, which would make it a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
estimate_agent_usageAInspectEstimate MockHero agent-plan cost before generating data. No login or API key is required; include an API key only when you want the estimate to use actual daily usage.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | Seed for reproducible output. | |
| scale | No | Multiplier for template record counts. | |
| format | No | json | |
| locale | No | Default locale such as en, de, fr, es, or ja. | |
| prompt | No | Plain-English data request. Example: 50 users and 200 orders linked to them. | |
| tables | No | ||
| api_key | No | Optional MockHero API key for no-auth MCP clients. Prefer the Authorization header when the client supports it. | |
| template | No | ||
| sql_dialect | No | ||
| daily_used_before | No | Optional assumption when no API key is supplied. | |
| estimated_records | No | Required for prompt estimates because this tool does not run prompt-to-schema conversion. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (all false), so the description carries the burden. It discloses that no login/API key is required and explains the optional API key's effect on using actual daily usage, which adds useful behavioral context beyond annotations. It stops short of describing side-effect behavior fully, but the 'before generating data' implies no data mutation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose and immediately useful behavioral details. No wasted words or redundant schema 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?
While the output schema covers return values, the description does not explain the different estimation modes (tables/template/prompt) or clarify how parameters like estimated_records are required for prompt-based estimates. Given the tool's complexity (11 params, three anyOf variants), this is a notable gap even with schema annotations.
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 64%, so baseline 3 is appropriate. The description does not explain the three estimation modes (tables, template, prompt) or which parameters to use for each, leaving the anyOf structure in the schema to carry that burden. The description adds no direct parameter-level information.
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 states 'Estimate MockHero agent-plan cost before generating data' with a clear verb and resource, and distinguishes it from sibling tools like generate_test_data and generate_from_template by focusing on cost estimation rather than data generation.
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 'before generating data' implies temporal context, and the note about when to include an API key provides decision guidance. However, it does not explicitly name alternative tools or state when not to use this tool, so it isn't a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_from_templateBInspectGenerate realistic test data from a pre-built MockHero template: ecommerce, blog, saas, or social. Small template previews up to 100 records can run free; larger or production usage requires a MockHero API key.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | ||
| scale | No | ||
| format | No | json | |
| locale | No | ||
| api_key | No | Optional MockHero API key for no-auth MCP clients. Prefer the Authorization header when the client supports it. | |
| template | Yes | ||
| sql_dialect | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations by disclosing the free-tier limit and API key requirement, which are not indicated by readOnlyHint, openWorldHint, idempotentHint, or destructiveHint. However, it does not explain side effects, resource consumption, error conditions, or whether the operation is deterministic despite idempotentHint=false. It adds some value but lacks comprehensive behavioral disclosure.
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 concise, with two sentences front-loading the core purpose and then adding essential usage context. It contains no fluff and every sentence earns its place, though it could potentially be more structured by explicitly linking the limit to a parameter.
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 7 parameters and an output schema, the description covers the essential template selection and free-tier limit but leaves out important contextual details such as how to control record count (scale), output format, localization, and SQL dialect. The output schema may cover return values, but the description alone is not fully complete for effective invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With only 14% schema description coverage, the description was expected to compensate for undocumented parameters. It does mention template types (duplicating the schema enum) and the 100-record limit, but it does not explain the meaning or usage of seed, scale, locale, format, sql_dialect, or their relationships. The description adds minimal semantic value beyond the schema, failing 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?
The description clearly states the tool's function: generating realistic test data from pre-built MockHero templates, listing specific template types (ecommerce, blog, saas, social). It distinguishes itself from sibling tools by focusing on pre-built templates, but does not explicitly differentiate from generate_test_data, so it lacks full sibling distinction.
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 usage conditions: free previews up to 100 records and API key requirement for larger/production use, which is useful context. However, it does not explicitly state when to prefer this tool over alternatives like generate_test_data, nor does it mention exclusions or alternative tools, leaving usage guidance implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_test_dataAInspectGenerate realistic JSON, CSV, or SQL test data from structured tables or a plain-English prompt. Explicit table schemas up to 100 records can run free as a proof-of-work preview; plain-English prompt generation, larger requests, and production usage require a MockHero API key.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | Seed for reproducible output. | |
| format | No | json | |
| locale | No | Default locale such as en, de, fr, es, or ja. | |
| prompt | No | Plain-English data request. Example: 50 users and 200 orders linked to them. | |
| tables | No | ||
| api_key | No | Optional MockHero API key for no-auth MCP clients. Prefer the Authorization header when the client supports it. | |
| sql_dialect | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (readOnlyHint=false, openWorldHint=true), so the description carries some burden. It adds valuable behavioral context: the free preview limit for table schemas, the requirement of an API key for prompt/larger requests, and the 'proof-of-work' nature of the free tier. These details go beyond what annotations indicate and help manage expectations. 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 sentences, front-loaded with the core purpose. Every phrase earns its place: formats, input methods, free-tier constraints, and API key requirements. No fluff or repetition. Exemplary 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 an output schema available, the description needn't detail return values. It covers the two input modes, output formats, key usage constraints, and the API key distinction. It does not explain sql_dialect or seed, but those are optional and schema-documented. The free preview vs production boundary is a critical piece of context that is included. Overall, it is sufficiently complete for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the main parameters by stating data sources ('structured tables', 'plain-English prompt') and output formats ('JSON, CSV, or SQL'). Schema coverage is 57%, so it partially compensates for undocumented parameters. The 'up to 100 records' hint clarifies the 'count' field context, and the API key requirement clarifies the api_key parameter. This adds useful semantic context 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 starts with a clear verb and resource: 'Generate realistic JSON, CSV, or SQL test data' and specifies two input methods ('structured tables or a plain-English prompt'). This precisely distinguishes it from sibling tools like generate_from_template or detect_schema. The purpose is unambiguous and action-oriented.
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?
Provides explicit context for when the tool can be used without an API key ('Explicit table schemas up to 100 records can run free') and when a key is required ('plain-English prompt generation, larger requests, and production usage'). This is practical usage guidance, though it does not mention alternative tools or exclusion criteria. The guidance is clear enough for selecting the tool in different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_field_typesAList MockHero field types, descriptions, parameters, and examples before building a schema.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds the output content (field types, descriptions, parameters, examples) but doesn't disclose additional behavioral traits like ordering or pagination. This adds modest value beyond the annotations, so a 3.
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 verb 'List' and immediately specifies the resource and content. Every word earns its place, making it highly concise and well-structured.
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 list tool with no parameters and existing annotations, the description is complete: it notes the content returned and the intended usage context. The output schema handles return value details, so no further explanation is needed.
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 baseline is 4. The description doesn't need to explain parameter semantics, and the empty schema already confirms no inputs are required.
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 lists MockHero field types, descriptions, parameters, and examples, and provides context for use before building a schema. It uses the specific verb 'List' and references a specific resource, distinguishing it from sibling tools like detect_schema and list_templates.
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 a clear context for when to use this tool ('before building a schema'), but it doesn't explicitly mention when not to use it or name alternative tools. This is clear context without exclusions, so a 4 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_templatesAList MockHero's pre-built schema templates for ecommerce, blog, SaaS, and social apps.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description confirms a read-only listing operation, consistent with the readOnlyHint annotation. It adds context that the templates are 'pre-built' and for specific domains, which goes beyond the annotation's basic safety profile. No contradictions; output schema covers return 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, well-structured sentence that front-loads the verb and object, then names the target domains. Every word earns its place 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?
For a zero-parameter, read-only listing tool with an output schema, the description is complete. It tells the agent exactly what is listed and the relevant categories, leaving no ambiguity about purpose or scope.
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 schema is empty and description need not explain parameter meanings. The baseline score for zero-parameter tools is 4, and no additional parameter info 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 uses the specific verb 'List' with a clear resource ('MockHero's pre-built schema templates') and enumerates the categories (ecommerce, blog, SaaS, social apps). This distinguishes it from sibling tools like list_field_types and generate_from_template.
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 clearly implies when to use the tool: when you need a pre-built schema template for the listed application categories. It does not explicitly mention alternatives or exclusions, but the context is clear enough for an agent to choose it over list_field_types or generate_from_template.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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