SF-MCP

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions authentication requirements in the parameter list, it doesn't describe the tool's behavior beyond the basic check—such as whether it returns a boolean, detailed permission info, error handling, rate limits, or side effects. For a permission-checking tool with zero annotation coverage, this leaves significant gaps in understanding how it operates.

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 well-structured and appropriately sized. It starts with a clear purpose statement, followed by a brief elaboration, and then a detailed parameter list. Every sentence earns its place, though the parameter explanations are lengthy but necessary given the schema coverage gap. It could be slightly more front-loaded with usage 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?

Given the complexity (8 parameters, 7 required) and the presence of an output schema (which means return values don't need explanation in the description), the description is fairly complete. It covers all parameters in detail and states the tool's purpose. However, it lacks behavioral context (e.g., how the check is performed, error cases) and usage guidelines, which are important for a tool with no 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?

The description includes a detailed 'Args' section that explains each of the 8 parameters, providing meaning beyond the input schema (which has 0% description coverage). It clarifies parameter purposes (e.g., 'instance: The SuccessFactors instance/company ID'), gives examples (e.g., for 'permission_type'), and notes defaults (e.g., 'locale: Locale for labels (default: en-US)'). This fully compensates for the lack of schema descriptions.

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: 'Check if a specific user has a particular permission' and 'Quickly verify whether a user has access to a specific feature or data.' This specifies the verb ('check/verify') and resource ('user permission/access'), but it doesn't explicitly differentiate from sibling tools like 'get_user_permissions' or 'get_role_permissions' that might retrieve permission lists rather than check a specific one.

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 alternatives. It doesn't mention sibling tools like 'get_user_permissions' (which might list all permissions for a user) or 'get_role_permissions' (which might check role-based permissions), nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name and parameters alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions authentication requirements and that the comparison returns match percentage and field differences, but does not cover other important aspects like rate limits, error handling, whether it's read-only or has side effects, or performance characteristics for large comparisons.

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 well-structured with a clear purpose statement, usage context, parameter documentation, and return value description. While comprehensive, it could be slightly more concise by combining some parameter explanations or using a more compact format for the similar data_center/environment pairs.

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 (10 parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description does an excellent job explaining parameters and purpose. However, it could provide more behavioral context about limitations, performance, or error scenarios to be fully complete for a complex comparison tool.

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

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

With 0% schema description coverage, the description fully compensates by providing clear explanations for all 10 parameters, including examples (e.g., 'dev instance', 'prod instance', 'User', 'DC55', 'preview') and clarifying that auth credentials are 'required, used for both instances'. This adds substantial value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific verb ('compare') and resource ('entity configuration/metadata between two SuccessFactors instances'), distinguishing it from sibling tools like 'get_configuration' (which retrieves single-instance data) or 'list_entities' (which lists available entities). It explicitly mentions the comparison scope across environments.

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 for when to use this tool ('verifying that dev/test/production environments are aligned before deployments, or for auditing configuration drift'), but does not explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., 'get_configuration' for single-instance checks).

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 of behavioral disclosure. While it mentions authentication requirements in the parameter list, it doesn't describe important behavioral aspects: whether this is a read-only operation, potential rate limits, what happens when no results are found, or how results are structured. For a tool with 10 parameters and no annotation coverage, this is a significant gap in behavioral 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?

The description is well-structured with a clear purpose statement followed by a parameter list. Every sentence earns its place, and the information is front-loaded. It could be slightly more concise by integrating the parameter explanations more seamlessly, but overall it's efficiently organized without 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?

Given the tool's complexity (10 parameters, 7 required) and the presence of an output schema, the description is reasonably complete. The parameter documentation is thorough, and the output schema will handle return value documentation. However, for a tool with no annotations and significant behavioral implications (authentication, data retrieval), the description could better address operational context like error conditions or result formatting.

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

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

With 0% schema description coverage, the description fully compensates by providing clear documentation for all 10 parameters. Each parameter is listed with meaningful explanations: date formats, example values, defaults, and constraints. The description adds substantial value beyond what the bare schema provides, explaining what each parameter means and how to use it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Find employees with upcoming work anniversaries for recognition programs.' This specifies the verb ('find'), resource ('employees'), and context ('for recognition programs'). However, it doesn't explicitly differentiate from sibling tools like 'get_new_hires' or 'get_terminations' which also retrieve employee data based on different criteria.

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 alternatives. With many sibling tools that retrieve employee data (e.g., 'get_new_hires', 'get_terminations', 'search_employees'), there's no indication of when anniversary searches are appropriate versus other employee queries. The description mentions the context ('for recognition programs') but doesn't provide explicit usage rules or exclusions.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions authentication requirements and result limits, which is helpful, but doesn't cover important aspects like whether this is a read-only operation, potential rate limits, error conditions, or what happens when parameters are invalid. For a tool with authentication and data retrieval, more behavioral context is needed.

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 well-structured and appropriately sized. It starts with the core purpose, then provides details about what information is shown, and finally lists all parameters with clear explanations. While efficient, the parameter section could be slightly more concise by grouping related authentication parameters together.

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 complexity (8 parameters, authentication requirements) and the presence of an output schema, the description is mostly complete. It covers authentication needs, result limits, and parameter meanings thoroughly. The main gap is lack of behavioral context about read-only nature, error handling, and rate limits, but the output schema reduces the need to describe return values.

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 provides excellent parameter semantics with 0% schema description coverage. It clearly explains what each parameter means ('SuccessFactors instance/company ID', 'job requisition ID', 'SAP data center code', etc.), provides examples for data_center, specifies default values for include_rejected and top, and indicates which parameters are required. This fully compensates for the lack of schema descriptions.

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 with specific verbs ('track candidates', 'shows all applicants') and resources ('for a job requisition', 'through hiring stages'). It distinguishes itself from sibling tools like 'get_open_requisitions' by focusing on candidate tracking rather than requisition listing.

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 alternatives. While it mentions what the tool does, it doesn't specify scenarios where it's preferred over other candidate-related tools or when other tools might be more appropriate. There's no mention of prerequisites or limitations beyond the parameter requirements.

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 of behavioral disclosure. It mentions authentication requirements (auth_user_id, auth_password) and a max limit for user_ids (20), which adds useful context. However, it lacks critical details: whether this is a read-only operation, potential rate limits, error conditions, or what the output schema contains. For a tool with sensitive compensation data and no annotations, this is a significant gap.

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 well-structured and appropriately sized. The first sentence states the purpose clearly, followed by a bullet-like 'Args:' section that efficiently documents parameters. Every sentence earns its place, with no redundant information. It could be slightly more front-loaded by integrating key constraints (e.g., max 20 IDs) into the purpose statement, but overall it's concise and effective.

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 (7 parameters, sensitive data, no annotations) and the presence of an output schema, the description is moderately complete. It thoroughly documents parameters but lacks behavioral context (e.g., security implications, error handling). The output schema mitigates the need to explain return values, but for a compensation tool with authentication, more guidance on usage and risks would improve completeness. It's adequate but has clear gaps.

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

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

Schema description coverage is 0%, so the description must compensate fully. It provides clear semantics for all 7 parameters: explaining what each represents (e.g., 'Employee user ID(s)', 'SAP data center code'), giving examples (e.g., 'DC55'), noting constraints ('max 20'), and specifying defaults ('Defaults to latest'). This adds substantial value beyond the bare schema. A 5 is reserved for exceptional detail like format specifics or interdependencies.

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: 'Get compensation breakdown for employees including base pay and pay components.' It specifies the verb ('Get'), resource ('compensation breakdown'), and scope ('for employees'), distinguishing it from sibling tools like get_employee_profile or get_employee_history. However, it doesn't explicitly differentiate from all siblings (e.g., query_odata might also retrieve compensation data), so it's not a perfect 5.

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 alternatives. It lists parameters but doesn't mention prerequisites, exclusions, or comparisons to sibling tools like get_employee_profile (which might include compensation) or query_odata (a generic query tool). Without such context, users must infer usage from the tool name and parameters alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's function (fetching metadata), output (field definitions, types, constraints), and authentication requirements (user ID and password). However, it does not mention potential rate limits, error conditions, or data sensitivity, leaving some behavioral aspects uncovered.

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 well-structured and front-loaded with the core purpose, followed by detailed parameter explanations and return value. Every sentence adds value—no fluff or repetition—making it efficient and easy to parse for an AI agent.

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 complexity (6 required parameters, no annotations, but with an output schema), the description is complete. It explains the tool's purpose, all parameters with examples, and the return type. The output schema handles return values, so the description appropriately focuses on usage and inputs without redundancy.

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 fully. It provides clear semantics for all 6 parameters: 'instance' as company ID, 'entity' as OData entity to inspect with examples, 'data_center' as SAP code with examples, 'environment' as type with examples, and authentication details as required. This adds significant value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Retrieve', 'fetches') and resources ('OData entity metadata/configuration', '$metadata document'). It distinguishes itself from siblings by focusing on entity metadata retrieval rather than operational data like employee profiles or permissions, making its scope explicit.

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 inspecting entity fields and constraints, but does not explicitly state when to use this tool versus alternatives like 'list_entities' or 'query_odata'. It provides context for metadata retrieval but lacks explicit exclusions or comparisons with sibling tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses authentication requirements and pagination behavior ('top' parameter with default and max), which adds useful context. However, it lacks details on rate limits, error handling, or response format, leaving behavioral gaps for a tool with authentication and data retrieval.

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 well-structured with a purpose statement followed by a parameter list. Each sentence adds value, but the parameter explanations could be more integrated into the flow rather than a separate 'Args:' section. Overall, it is appropriately sized 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 complexity (authentication, multiple parameters) and the presence of an output schema, the description is largely complete. It covers authentication needs and parameter meanings, though it could benefit from more behavioral context. The output schema likely handles return values, reducing the description's burden.

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 provides clear semantics for all 6 parameters: explains what 'instance', 'data_center', 'environment', 'auth_user_id', 'auth_password', and 'top' represent, including examples and constraints. This fully addresses the schema's lack of descriptions.

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: 'List all dynamic permission groups in the instance.' It specifies the verb ('List') and resource ('dynamic permission groups'), and adds context about their use in RBP. However, it does not explicitly differentiate from sibling tools like 'get_rbp_roles' or 'get_permission_metadata', which prevents a score of 5.

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 alternatives. It mentions that dynamic groups are used in RBP, but does not specify scenarios, prerequisites, or exclusions. With many sibling tools related to permissions and roles, this lack of differentiation is a significant gap.

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

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

With no annotations provided, the description carries full burden. It discloses that authentication credentials are required (auth_user_id, auth_password) which is crucial behavioral context. However, it doesn't mention rate limits, pagination, error handling, or whether this is a read-only operation (though 'View' implies reading). The description adds some value but leaves gaps in 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 well-structured with purpose first, then details, then parameter explanations. It's appropriately sized for a 7-parameter tool with authentication requirements. Minor improvement could be front-loading the authentication requirement more prominently, but overall it's efficient with minimal waste.

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 (7 parameters, authentication, compensation option) and presence of an output schema (which handles return values), the description is quite complete. It covers purpose, usage context, and all parameter semantics. The main gap is lack of behavioral details like rate limits or error handling, but with an output schema, the description doesn't need to explain return values.

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

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

With 0% schema description coverage, the description fully compensates by providing clear semantic explanations for all 7 parameters. Each parameter gets a meaningful description that explains what it represents (e.g., 'SAP data center code', 'Environment type', 'If True, also fetches salary history'), adding substantial value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('View', 'Shows') and resources ('employee's job history', 'chronological job records'). It distinguishes this tool from siblings like 'get_employee_profile' or 'get_compensation_details' by focusing specifically on historical job changes rather than current data or compensation details.

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 this tool ('Useful for reviewing career progression'), but doesn't explicitly state when NOT to use it or mention specific alternatives. It doesn't compare against siblings like 'get_employee_profile' (current data) or 'get_compensation_details' (compensation focus), though the compensation parameter hints at overlap.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns data in a single call and includes optional compensation, but lacks details on rate limits, authentication behavior, error handling, or data freshness. It doesn't contradict annotations, but provides only basic operational 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 appropriately sized and front-loaded with the core purpose first, followed by return details and parameter explanations. Every sentence adds value, though the parameter section is somewhat lengthy but necessary given the lack of schema descriptions.

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 complexity (7 parameters, no annotations) and the presence of an output schema, the description is reasonably complete. It explains the tool's purpose, return data, and all parameters semantically. The output schema likely covers return values, so the description doesn't need to detail them further.

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 fully. It provides clear semantic explanations for all 7 parameters, including examples (e.g., 'jsmith', 'DC55'), required status, and the purpose of 'include_compensation'. This adds significant value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get a complete employee profile') and resources ('employee profile including job info, contact details, and manager'). It distinguishes from siblings like 'get_employee_history' or 'get_compensation_details' by specifying it returns current comprehensive data in a single call.

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 by listing what data is returned and mentioning an optional compensation inclusion, but it doesn't explicitly state when to use this tool versus alternatives like 'get_employee_history' for historical data or 'get_compensation_details' for compensation-only queries. No explicit exclusions or prerequisites are provided beyond parameter requirements.

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

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

With no annotations provided, the description carries full burden. It clearly indicates this is a read operation (finding/checking) and specifies authentication requirements. However, it doesn't disclose rate limits, pagination behavior beyond 'top' parameter, or what happens when no results are found. It adds some behavioral context but leaves gaps.

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

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

The description is well-structured with purpose statement, field checks, and detailed parameter documentation. While comprehensive, it's appropriately sized for an 8-parameter tool. Every sentence serves a purpose, though the parameter section is lengthy but necessary given the schema coverage gap.

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 complexity (8 parameters, authentication requirements) and 0% schema coverage, the description does an excellent job explaining inputs. With an output schema present, return values don't need explanation. The main gap is lack of behavioral details like rate limits or error handling, but overall it's quite complete for the agent's needs.

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

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

With 0% schema description coverage, the description fully compensates by providing detailed parameter explanations. Each of the 8 parameters is clearly documented with examples, defaults, and requirements. The description adds substantial value beyond the bare schema, explaining field options, data center codes, environment types, and authentication needs.

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 specific purpose: 'Find employees with incomplete profiles for compliance audits' with explicit verb 'find' and resource 'employees with incomplete profiles'. It distinguishes from siblings by focusing on missing data checks rather than general employee retrieval or other HR functions.

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 for usage ('for compliance audits') and specifies what fields are checked, but doesn't explicitly state when NOT to use this tool or name specific alternatives among the sibling tools. The context is well-defined but lacks explicit exclusion 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions authentication requirements and default/max values for 'top', but doesn't cover rate limits, pagination, error handling, or what specific data fields are returned. For a tool with 9 parameters and no annotation coverage, this leaves significant behavioral gaps.

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

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

The description is well-structured with purpose statement upfront, followed by detailed parameter documentation. Every sentence serves a purpose, though the Args section is quite lengthy due to 9 parameters. It could be more concise by grouping related parameters, but overall it's efficiently organized.

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 complexity (9 parameters, authentication required) and the presence of an output schema, the description is reasonably complete. It thoroughly documents all parameters and their semantics. The main gap is lack of behavioral context (rate limits, error handling), but the output schema reduces need to describe return values.

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

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

With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation in the Args section. Each parameter is clearly explained with examples (e.g., 'YYYY-MM-DD', 'DC55'), defaults ('default: 100'), constraints ('max: 500'), and requirements ('required'). This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'List recent and upcoming new hires for onboarding planning' and 'Shows employees hired within a date range with their job details.' This specifies the verb ('list', 'shows'), resource ('new hires', 'employees'), and scope ('recent and upcoming', 'within a date range', 'with job details'), distinguishing it from sibling tools like get_terminations or get_employee_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 context ('for onboarding planning') and date-range filtering, but doesn't explicitly state when to use this tool versus alternatives like search_employees or get_employee_history. It provides basic functional context but lacks explicit guidance on tool 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 provided, the description carries the full burden of behavioral disclosure. It describes the tool as a listing operation with filtering capabilities, which implies read-only behavior, but does not address authentication requirements (beyond parameter listing), rate limits, pagination, or error handling. The mention of 'default: 100, max: 500' for 'top' hints at result limits, but overall behavioral context is minimal.

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 well-structured with a clear purpose statement followed by a detailed parameter breakdown. Every sentence earns its place, but the 'Args' section is lengthy due to the high parameter count, which is necessary given the lack of schema descriptions. It could be more front-loaded with key usage notes, but overall it is 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 tool with 10 parameters, 0% schema coverage, no annotations, but an output schema, the description is reasonably complete. It covers all parameters in detail and states the tool's purpose, though it lacks behavioral context like authentication flow or error handling. The presence of an output schema reduces the need to describe return values, making this adequate but not exhaustive.

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?

Given 0% schema description coverage, the description fully compensates by providing detailed parameter semantics in the 'Args' section. It explains each of the 10 parameters, including purposes (e.g., 'Filter by department'), examples (e.g., 'DC55'), defaults (e.g., 'default: open'), and constraints (e.g., 'max: 500'), adding significant value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('List job requisitions') and resources ('job requisitions with status and hiring manager'), and distinguishes it from siblings by focusing on requisitions rather than employees, permissions, or other HR data. The second sentence elaborates on the scope ('open or all job requisitions for tracking the hiring pipeline'), making it 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 context ('for tracking the hiring pipeline') but does not explicitly state when to use this tool versus alternatives like 'get_candidate_pipeline' or 'search_employees'. It provides no guidance on prerequisites or exclusions, leaving the agent to infer based on the tool name and parameters.

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 of behavioral disclosure. While it mentions authentication requirements and filtering parameters, it doesn't describe important behavioral aspects: whether this is a read-only operation, potential rate limits, pagination behavior beyond the 'top' parameter, error conditions, or what the output contains. For a tool with 10 parameters and authentication needs, this leaves significant gaps in understanding how the tool behaves.

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 well-structured with a clear purpose statement followed by organized parameter documentation. Each parameter explanation is brief and focused. While slightly longer due to the 10 parameters, every sentence serves a purpose. The structure helps users quickly understand both what the tool does and how to use it.

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 complexity (10 parameters, authentication requirements) and the presence of an output schema, the description is moderately complete. It covers authentication needs and filtering parameters adequately, and the output schema will handle return value documentation. However, for a tool with no annotations and significant behavioral implications (tracking performance data across an organization), the description should provide more context about limitations, security considerations, or typical use cases.

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

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

With 0% schema description coverage, the description compensates well by providing clear explanations for all 10 parameters. Each parameter gets a brief but meaningful explanation, including examples for 'data_center', default values, and filtering logic (e.g., 'applied client-side' for department/manager filters). The description adds substantial value beyond the bare schema, though it could provide more detail on parameter formats or constraints.

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: 'Track performance review form completion across the organization.' This specifies the verb ('track') and resource ('performance review form completion') with organizational scope. However, it doesn't explicitly differentiate from sibling tools like 'get_employee_profile' or 'search_employees' that might also retrieve performance data, leaving some ambiguity about when this specific tool is uniquely appropriate.

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 alternatives. With many sibling tools available (e.g., 'get_employee_profile', 'search_employees', 'query_odata'), there's no indication of whether this is the primary method for performance review data or if other tools might be better for specific scenarios. The description lacks any 'when-to-use' or 'when-not-to-use' context.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation (implying read-only) but doesn't explicitly confirm it's non-destructive. It mentions authentication parameters are 'required' but doesn't describe error handling, rate limits, or what the output contains beyond 'mapping technical IDs to human-readable labels.' For a tool with authentication requirements, more behavioral context is needed.

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 well-structured and appropriately sized. The first paragraph states the purpose, the second adds context, and the 'Args:' section clearly documents parameters. While efficient, the 'Args:' formatting is slightly verbose compared to integrating parameter details into flowing text, but all content 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?

Given the tool has an output schema (which handles return values), no annotations, and 6 parameters with 0% schema coverage, the description does well on parameters but lacks behavioral context. It covers authentication needs and the mapping purpose but doesn't address error cases, performance, or how this metadata integrates with RBP analysis. For a security-related tool with authentication, more completeness would be beneficial.

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 fully compensate. It provides clear semantics for all 6 parameters: 'instance' (SuccessFactors instance/company ID), 'data_center' (SAP data center code with examples), 'environment' (environment type with examples), 'auth_user_id' (SuccessFactors user ID for authentication), 'auth_password' (SuccessFactors password for authentication), and 'locale' (locale for labels with default). This adds substantial value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get the mapping of permission type labels for RBP security analysis.' It specifies the verb ('Get'), resource ('mapping of permission type labels'), and context ('for RBP security analysis'). This distinguishes it from sibling tools like 'get_user_permissions' or 'get_role_permissions' which focus on user/role-specific permissions rather than metadata mappings.

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 alternatives. It mentions the tool helps 'understand what permission categories exist in the system,' but doesn't specify scenarios where this metadata is needed (e.g., before analyzing permissions) or contrast it with related tools like 'get_rbp_roles' or 'get_role_permissions' that might provide overlapping or complementary data.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions authentication requirements and includes_inactive parameter, but doesn't cover important aspects like rate limits, error handling, response format, pagination, or whether this is a read-only operation. For an 8-parameter tool with authentication needs, this leaves significant behavioral gaps.

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

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

The description is well-structured with clear sections: purpose explanation, parameter documentation, and common examples. While comprehensive, some sentences could be more concise (e.g., 'Picklists are used throughout SuccessFactors for dropdown fields' could be combined with the first sentence). Overall, it's appropriately sized for an 8-parameter tool with complex authentication requirements.

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 complexity (8 parameters, authentication requirements) and the presence of an output schema, the description is reasonably complete. It thoroughly documents parameters and provides context about picklist usage. However, it could better address behavioral aspects like authentication scope, error conditions, or performance characteristics to be fully comprehensive for this type of enterprise API 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 provides excellent parameter semantics despite 0% schema description coverage. It explains each parameter's purpose with examples (e.g., picklist_id examples like 'ecJobFunction', data_center codes like 'DC55'), clarifies required vs optional parameters, provides default values, and even includes a helpful list of common picklists with their meanings. This fully compensates for the lack of schema descriptions.

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 with a specific verb ('Get') and resource ('values for a specific picklist'), and distinguishes it from siblings by explaining picklists are for dropdown fields in SuccessFactors. It provides essential context about data validation and understanding available field values, making the 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 provides no guidance on when to use this tool versus alternatives. While it mentions picklists are used throughout SuccessFactors, it doesn't specify scenarios where this tool is preferred over other data retrieval tools like get_employee_profile or query_odata. There's no mention of prerequisites or constraints beyond the required parameters.

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 states this is a read operation ('Get all', 'Lists all') but doesn't disclose behavioral traits like authentication requirements (implied by parameters but not explicitly stated), rate limits, pagination, error conditions, or what 'all' means in practice (e.g., whether it returns all roles at once or requires pagination).

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?

Well-structured with a clear purpose statement, bullet-point examples, and a dedicated Args section. The description is appropriately sized (7 sentences) and front-loaded with the core functionality. Minor redundancy exists (e.g., 'Get all' and 'Lists all' are repetitive).

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 annotations, 6 parameters with 0% schema coverage, and an output schema present, the description does well on parameters but lacks behavioral context (authentication needs, rate limits, pagination). The output schema likely covers return values, so that gap is acceptable, but for a tool with authentication parameters and no annotations, more operational guidance would help.

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 provides meaningful context for all 6 parameters: explains 'instance' as company ID, 'data_center' with examples, 'environment' with enum-like values, clarifies auth parameters as 'required', and describes what 'include_description' does. This adds substantial value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get all') and resource ('Role-Based Permission (RBP) roles in the instance'), with specific examples of role types (admin, HR, manager, employee self-service). It distinguishes from siblings like 'get_user_roles' by focusing on system-wide security roles rather than user-specific assignments.

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_user_roles' or 'get_permission_metadata'. The description mentions listing 'all security roles' but doesn't specify use cases, prerequisites, or exclusions relative to 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does mention authentication requirements and filtering capabilities, but lacks details about rate limits, pagination behavior beyond the 'top' parameter, error conditions, or what specific data fields are returned in the history records. The description is functional but not comprehensive.

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 well-structured with a clear purpose statement followed by detailed parameter documentation. While somewhat lengthy due to the 10 parameters, every sentence adds value. The front-loaded purpose statement is effective, and the parameter section is organized for clarity.

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

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

For a 10-parameter tool with no annotations, the description provides substantial context about authentication, filtering, and date formats. The existence of an output schema reduces the need to describe return values. The main gap is lack of behavioral details like rate limits or error handling, but overall this is reasonably complete for an audit-focused query 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?

Given 0% schema description coverage, the description compensates well by documenting all 10 parameters with clear explanations. It provides format details (ISO date format), optional/required status, default values, and practical examples (e.g., 'DC55', 'DC10'). The only gap is lack of enum values for 'environment' and 'data_center' parameters.

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 with specific verbs ('get', 'shows') and resources ('history of role assignments', 'RBP roles to users'). It distinguishes this tool from siblings like 'get_user_roles' or 'get_rbp_roles' by focusing specifically on the historical audit trail of role assignments rather than current state.

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 alternatives like 'get_user_roles' or 'get_rbp_roles'. While it mentions the tool helps with auditing, it doesn't specify scenarios where this historical view is preferable to current-state queries, nor does it mention prerequisites or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it returns audit history including who, when, and what changes, with filtering capabilities and pagination (top parameter). It mentions authentication requirements and default/max values for 'top,' though it doesn't cover error handling, rate limits, or data freshness.

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 well-structured with purpose first, followed by return details, usage context, and a clear parameter list. It's appropriately sized for a 10-parameter tool, though the 'Args:' section is lengthy but necessary. Every sentence earns its place, but some redundancy exists (e.g., 'optional' is stated multiple times).

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 tool with 10 parameters, 0% schema coverage, no annotations, but with an output schema, the description is largely complete. It covers purpose, behavior, and detailed parameter semantics. The output schema likely handles return values, so the description doesn't need to explain them. Minor gaps include lack of error cases or performance characteristics.

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?

Given 0% schema description coverage, the description compensates fully by providing detailed semantics for all 10 parameters. It explains each parameter's purpose (e.g., 'instance: The SuccessFactors instance/company ID'), format constraints (e.g., ISO format for dates), optionality, defaults (top default 100, max 500), and relationships (role_name as alternative to role_id). This adds significant value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verb ('Get modification history') and resource ('RBP roles'), distinguishing it from sibling tools like 'get_rbp_roles' (which likely lists roles) and 'get_role_assignment_history' (which likely tracks user assignments). The second sentence elaborates on what information is returned, reinforcing the audit-focused 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?

The description implies usage context through 'This helps audit role configuration changes over time,' suggesting it's for auditing purposes. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_rbp_roles' or 'get_role_assignment_history,' nor does it mention prerequisites or exclusions beyond the required authentication parameters.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions authentication requirements in the Args section, it doesn't describe rate limits, error conditions, response format, or what 'detailed permissions' actually includes. For a tool with 7 parameters and no annotation coverage, this is insufficient.

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 appropriately sized and front-loaded with the core purpose first, followed by usage context, then detailed parameters. The Args section is comprehensive but necessary given the parameter count. One minor improvement could be integrating parameter explanations more seamlessly.

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 complexity (7 parameters, no annotations, but with output schema), the description provides good coverage of inputs and purpose. The existence of an output schema means return values don't need explanation. However, more behavioral context would improve completeness for this authentication-required tool.

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

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

With 0% schema description coverage, the description fully compensates by providing detailed parameter explanations in the Args section. Each parameter gets clear semantic context with examples (e.g., 'e.g., "10"', 'e.g., 'DC55'', 'default: en-US'), adding substantial value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get detailed permissions') and resource ('for a specific RBP role'), distinguishing it from sibling tools like get_user_permissions or get_rbp_roles. It explicitly mentions the purpose is for security audits, providing clear 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?

The description provides clear context about when to use this tool ('essential for security audits'), but doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools. It implies usage for role permission analysis without comparative 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?

With no annotations provided, the description carries full burden. It discloses authentication requirements (auth_user_id and auth_password are required) and some behavioral traits (default values, maximum limits for 'top' parameter). However, it doesn't mention rate limits, error conditions, pagination behavior, or what 'active' means operationally.

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 well-structured with purpose statement, use cases, and parameter documentation. Every sentence adds value, though the parameter section is quite detailed (necessary given schema gaps). It could be slightly more concise by integrating some parameter details into the main description.

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

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

Given the complexity (8 parameters, authentication requirements) and presence of an output schema, the description provides substantial context. It covers authentication, parameter meanings, use cases, and data scope. The main gap is lack of behavioral details like rate limits or error handling, but the output schema reduces need to describe return values.

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

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

With 0% schema description coverage for 8 parameters, the description fully compensates by providing detailed semantic explanations for every parameter. It clarifies what each parameter represents (e.g., 'SAP data center code', 'Environment type', 'Maximum direct reports to return'), including default values and constraints that aren't in 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 specific action ('Get a manager's team roster') and resource ('with direct (and optionally indirect) reports'), distinguishing it from sibling tools like get_employee_profile or search_employees. It explicitly mentions what data is returned (job title, department, location, hire date) and the scope (active team members).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.