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

With no annotations, the description carries the behavioral burden. It discloses an important non-obvious behavior: automatic paging through the entire result set and combining the results. It implies a non-mutating fetch, though it could add more about response shape or error 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?

Two sentences, front-loaded with the action and resource, followed by the key behavioral guarantee and a concrete use case. Every sentence earns its place and there is no filler.

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

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

For a low-complexity tool with one optional parameter and no output schema, the description is largely sufficient: it explains behavior, scope, and purpose. It does not describe the return envelope or field names, but the mapping use case implies the relevant output and the missing details are not essential for invoking it.

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

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

The single parameter pageSize is fully described in the schema (type, bounds, default), so the description adds no additional parameter meaning. The description focuses on overall behavior rather than pageSize, which is acceptable given 100% schema coverage.

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

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

The description states a specific verb ('Fetch all') and resource ('PSA clients/customers from Keka'), and it explains what the result is used for. The resource name clearly distinguishes it from sibling get_* tools.

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

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a concrete use case: mapping a project's clientId from get_projects to a readable client/customer name for reporting. It does not explicitly state when not to use this tool or mention alternatives, but the sibling tools are distinct resources, so the context is clear.

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 meaningfully reveals that the tool automatically pages through the entire result set and combines it, which is non-obvious behavior. It does not discuss authentication, rate limits, or exact response shape, but for a simple read-only fetch the pagination disclosure is the most important behavioral trait.

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 tight sentences deliver the core behavior, pagination detail, and a concrete use case without any filler. The action and resource are front-loaded, and every clause 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 single-optional-parameter fetch tool with no output schema, the description is complete enough: it explains what is fetched, how pagination is handled, and what the results should be used for. The stated mapping use case also implies the important return fields (employee ID, name, job title, employment status), so an agent can call the tool and use its result appropriately.

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

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

There is only one optional parameter, pageSize, and the input schema already describes it completely, including default and maximum values. The description's mention of automatic paging complements the schema, but it does not add new parameter-level meaning beyond what the schema already covers.

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 opens with a specific verb and resource: 'Fetch all employees from Keka HRIS.' It also clarifies scope (all employees, no filters) and introduces the intended mapping purpose, which clearly distinguishes it from sibling tools that handle timesheets, projects, clients, leaves, and holidays.

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 an explicit use case: 'Use this to map employee IDs found in timesheet data to names, job titles, and employment status.' It does not explicitly list when not to use it or name alternative tools, but the sibling set is sufficiently distinct and the stated use case is concrete enough for an agent to select it correctly.

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 present, the description carries the full burden and does so exceptionally well. It discloses that multiple calendars are queried and combined, that results are tagged with origin calendar metadata, and that pagination is handled automatically.

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

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

Three dense sentences with no filler. The purpose is front-loaded, and each subsequent sentence adds behavior an agent needs to know before calling the tool.

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

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

The description covers scoping, combining, tagging, and pagination, which is strong for a read-style lookup tool with no output schema. It could go further by naming the returned holiday fields, but an agent can invoke the tool correctly with the information given.

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%, but the description adds meaningful behavioral context beyond the schema, particularly for calendarId omission and automatic pagination. This lifts it above the baseline without fully detailing every parameter's edge behavior.

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 opens with a specific verb and resource: 'Fetch holidays from Keka's holiday calendar(s).' It clearly differentiates itself from sibling tools by explaining the per-calendar scoping behavior rather than an org-wide holiday lookup.

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 the tool and how optional parameters change its behavior, especially the calendarId omission path. It does not explicitly name alternatives like get_leaves, so it stops short of full 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 the full behavioral burden, and it delivers substantial detail: automatic paging, combined results, a 60-day range limit validated before calling Keka, default date behavior, and preservation of the numeric status field without guessed labels. It does not cover auth, rate limits, or errors, but it is far more transparent than a minimal fetch description.

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

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

Three sentences, each earning its place: the first states the core purpose, the second covers paging behavior, and the third explains date constraints and status handling. The description is front-loaded and contains no filler.

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

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

For a read-oriented list tool with four optional parameters and full schema coverage, the description provides the key call-time context: paging, range validation, defaults, and status semantics. The lack of an output schema is not fully compensated because the agent does not learn what fields each leave request contains, but the operational details needed to invoke it correctly are present.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful extra semantics by disclosing the 60-day maximum range and the automatic paging behavior that affects how from/to and pageSize are used. It does not add much for employeeIds, but the schema already documents those parameters fully.

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 ('Fetch') plus a clear resource ('leave requests across the organization from Keka for a date range'). This immediately distinguishes it from sibling tools like get_timesheet_entries, get_employees, and get_holidays, and it does not merely restate the title.

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 this is the tool for org-wide leave requests over a date range, but it never explicitly states when to prefer it over alternatives or when not to use it. There is no explicit alternative routing or exclusion guidance, so usage is mostly inferred from the resource name.

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

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses a notable behavior: 'Automatically pages through the entire result set and returns it combined.' This goes beyond the schema and helps the agent understand performance and aggregation semantics. It does not describe error behavior or exact return shape, but for a simple fetch tool this is a reasonable and valuable level of 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 three short, purposeful sentences. It opens with the action and resource, then states the pagination behavior, then provides the primary use case. There is no filler or redundant restatement of the title.

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 that this is a low-complexity tool with one optional parameter and no output schema, the description covers the essential context: what is fetched, the automatic pagination behavior, and the expected use case. It does not spell out the exact return fields, but it implies project IDs and names through the mapping use case, which is sufficient for an agent to call it correctly.

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

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

The schema already has 100% coverage, including a clear description of pageSize: 'Page size used internally per request to Keka (max 200, default 100).' The tool description itself does not add meaning beyond the schema, so the baseline score of 3 is appropriate.

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

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

The description states a specific verb and resource: 'Fetch all PSA projects from Keka.' It further clarifies the scope ('all PSA projects') and gives an explicit use case: mapping project IDs from timesheet data to project names. This clearly distinguishes it from sibling tools like get_timesheet_entries or get_clients by resource and intent.

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 clear contextual guidance: 'Use this to map project IDs found in timesheet data to project names.' This tells an agent when the tool is appropriate, but it does not explicitly name alternatives or state when not to use it. The instruction is clear context without exclusions, so it falls just short of a 5.

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

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

With no annotations, the description carries the behavioral disclosure burden and does it well: it reveals automatic pagination through the full result set, combined returns, and a 60-day range validation before calling Keka. This goes beyond what the schema alone conveys.

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

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

Two sentences with no filler. The core action is front-loaded, and the behavioral notes (pagination, range validation) are placed right after, each earning 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 fetch tool with five well-documented parameters, the description covers the key operational behaviors: scope, pagination, and validation. No output schema exists, but the description does not need to enumerate fields since it clearly states the returned data is the combined result set.

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

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

Schema description coverage is 100%, so the schema already documents all five parameters clearly. The description adds useful contextual details (auto-pagination, combined result, date-range limit) but does not add per-parameter meaning beyond the schema baseline.

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?

States a specific verb ('Fetch') and resource ('PSA timesheet entries for a single project from Keka'), which clearly distinguishes it from sibling tools like get_timesheet_entries. The single-project scope is explicit and 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 clearly implies when to use this tool: when fetching entries for a single project. It provides context about pagination and date constraints but does not explicitly name alternative tools or state when not to use it.

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

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

With no annotations, the description carries the full transparency burden and does so well. It explicitly discloses auto-paging through the entire result set, that callers never need to loop on pageNumber, the 60-day range validation, and the best-effort statusLabel enrichment alongside the preserved numeric status. These are meaningful behaviors beyond what the schema or name implies.

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

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

Three sentences, all dense with useful information: purpose first, then auto-paging behavior, then status field handling. No filler or repetition of schema details.

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

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

The tool has moderate complexity, no output schema, and no annotations, but the description covers the critical behavioral aspects an agent needs: scope, paging, date-range limit, and status field semantics. The only notable gap is the lack of explicit differentiation from get_project_time_entries, but it is not essential for making a correct call.

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

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

The input schema already covers 100% of parameters with descriptions, so the baseline is 3. The description adds real value by clarifying pageSize 'does not limit total results' and by noting the 60-day range validation that constrains from/to, which supplements the schema's date defaults.

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 opens with a specific verb+resource: 'Fetch PSA timesheet entries from Keka across employees/projects for a date range.' This clearly names the tool's domain and scope. It does not explicitly contrast itself with sibling get_project_time_entries, but the 'PSA timesheet entries across employees/projects' phrasing provides enough operational context to be distinct.

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 clear usage context: call this for PSA timesheet entries over a date range, with automatic paging handled internally. It does not state when not to use it or name alternatives, but the scope is specific enough that an agent can infer the appropriate situation.

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