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

No annotations provided, so description carries full burden. It discloses default 'todo' state, return value with id, and backend dependency (OPS_TASK_BACKEND). Could add more on permissions or side effects.

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?

Front-loaded with key information. Some extra details (like OPS_TASK_BACKEND) are relevant but could be more concise. Still, every sentence adds value.

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

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

No output schema, but description covers return value (created task with id) and backend context. Missing details on error handling or pagination, but adequate for a creation tool.

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

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

Schema coverage is 0%, but description adds significant meaning: explains project/assignee accept name/fragment/id, due_date format, and which parameters are required/optional.

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

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

The description explicitly states 'Create a new task on a project' and lists concrete use cases ('work item, to-do, action item'). It distinguishes itself from sibling tools like update_task_status.

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?

Clearly says when to use the tool and mentions update_task_status for changing status afterward. Lacks explicit 'do not use when' but provides sufficient context.

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

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

No annotations provided, so description carries full burden. It explains the return values but does not disclose side effects, auth requirements, or error handling. It implies read-only behavior via 'report' but not explicitly.

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

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

Two sentences, front-loaded with purpose and output details. Every word adds value; no redundancy.

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

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

Given one parameter, no output schema, and no annotations, the description covers the main use case and parameter flexibility. It lacks potential error or edge-case info, but is largely complete for a simple report 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 single parameter 'project' is described as accepting a name, unique name fragment, or numeric id—info entirely missing from the input schema. With 0% schema coverage, this description is essential and adds full meaning beyond the schema.

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

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

The description clearly states it reports on how a single project tracks against its budget, listing returned fields (metadata, budget_hours, logged_hours, remaining_hours) and gives an example query. This distinguishes it from siblings like list_projects or utilization_report.

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 specifies use cases: 'how is Orion tracking against budget' or before logging significant time. It implicitly suggests when to use but does not explicitly mention alternatives or when not to use.

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, but description accurately describes a simple read operation with no side effects. Could be more explicit about being read-only or non-destructive, but sufficient.

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 efficient sentences. Front-loaded with primary purpose, followed by usage guidance. No unnecessary words.

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

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

Complete for a no-parameter tool with an output schema. Covers purpose, usage, and return fields. No gaps given the simplicity.

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?

No parameters, schema coverage 100%. Description confirms no arguments, adding no extra meaning but confirming the fact.

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?

Clearly states 'List every employee in the platform' with specific fields. Distinguishes from sibling tools which focus on tasks, projects, and time logging.

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

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

Explicitly states use cases: discovering employees, looking up IDs/names, checking capacity. Says to filter manually for subsets. No explicit when-not-to-use, but 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, the description must disclose behavioral traits. It states 'Takes no arguments and returns the full project list', which is transparent about its input and output scope. However, it does not mention authorization, rate limits, or that it is a read-only operation, but for a simple list tool, this is adequate.

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 sentences: first sentence lists what is returned, second sentence explains usage, third sentence confirms no arguments. Each sentence is essential and well-front-loaded. No wasted words.

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

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

Given the tool is simple (0 parameters, output schema exists), the description covers purpose, usage, and output fields completely. It tells the agent exactly what to expect and when to use 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 tool has 0 parameters with 100% schema description coverage. Since there are no parameters, the baseline is 4. The description correctly notes 'Takes no arguments', adding no redundant information.

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

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

The description explicitly states 'List every project with its id, name, client, status (active, on_hold, or closed), and budget_hours', providing a specific verb+resource with detailed fields. It distinguishes from sibling tools by mentioning use cases like finding project id before creating tasks, logging time, or requesting hours report, which implies differentiation from tools like create_task, log_time, and get_project_hours.

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 states when to use: 'Use this to discover which projects exist or to find a project's id or exact name before creating tasks, logging time, or requesting an hours report.' It does not explicitly mention when not to use or alternatives, but the context of sibling tools makes it clear enough.

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 present, so the description carries full burden. It discloses that the backend is dynamic (OPS_TASK_BACKEND) and that the return includes project and assignee names for readability. This provides useful context beyond the basic list operation, though it omits potential behavioral traits like rate limits or ordering.

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

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

The description is concise (5 sentences) and front-loaded with the core purpose and filters. Every sentence adds value: use cases, parameter details, filter logic, return info, and backend note. No redundancy or fluff.

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

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

With no annotations but an output schema present, the description explains the return includes project and assignee names, mentions the dynamic backend, and covers filter behavior. However, it does not address pagination, limit, or sorting, which are typical for list tools.

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

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

Schema coverage is 0%, but the description adds rich semantics: project and assignee accept 'a name, a unique name fragment, or an id', and status must be exact enums 'todo, in_progress, or done'. This fully compensates for the schema's lack of specificity, making parameters usable.

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 clear verb+resource: 'List tasks, optionally narrowed by project, assignee, and/or status.' It then provides concrete use cases (e.g., 'what is on someone's plate') and mentions finding a task ID for updates, distinguishing it from sibling tools like create_task or update_task_status by focusing on reading.

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 explicitly states when to use the tool: 'Use this to answer what is on someone's plate, what work remains on a project, or to find a task's id before updating its status.' It also explains how filters combine (AND) and parameter formats. However, it does not contrast with siblings like list_projects or provide exclusion cases.

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 provided; the description carries the full burden. It discloses the mutation effect (logging hours), the return value (created entry with id), and the immediate impact on other tools (get_project_hours and utilization_report). It does not discuss potential side effects like overwriting existing entries, but the behavior is straightforward.

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

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

The description is concise: just a few sentences without unnecessary detail. It is front-loaded with purpose, then usage, then parameter details, return value, and downstream effects. Every sentence 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 moderate complexity (5 simple parameters, no output schema), the description covers purpose, usage, parameter semantics, and effects. It does not discuss error handling or edge cases, but those are generally beyond expectations. The inclusion of downstream tool visibility adds completeness.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaning to all parameters: explains that employee and project accept names, fragments, or numeric IDs; date format YYYY-MM-DD; hours range (0 to 24); note optional. This is thorough and goes beyond the schema's type-only information.

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

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

The description clearly states the tool's action: 'Log hours that an employee worked on a project for a specific day.' It uses a specific verb (log) and resource (hours), and distinguishes itself from sibling tools like get_project_hours (read) and create_task (different domain).

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

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

The description provides explicit usage context: 'Use this when someone reports time worked or asks you to record effort.' It does not list explicit alternatives or when-not-to-use, but the context is clear and sufficient given the sibling list.

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 provided, so description carries full burden. It discloses that the tool requires task_id and status, returns the full updated task for confirmation. Could be slightly improved by explicitly noting the mutation is destructive, but the update nature is implied. A minor 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?

Three sentences, front-loaded with action and allowed values. Every sentence adds value: what it does, when to use, prerequisites, return value. No 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?

For a simple tool with two required parameters and no output schema, the description covers purpose, usage, prerequisites, and response. It is complete and leaves no ambiguity.

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

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

Schema coverage is 0%, but description adds meaning beyond schema: it specifies that task_id is required and explains how to obtain it, and lists the allowed status values (todo, in_progress, done). It also clarifies ID formats for different backends. Only missing explicit mapping of parameter names to 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 moves a task to a new status (todo, in_progress, done) and lists example use cases (start, finish, reopen). It distinguishes from sibling tools by explaining that list_tasks should be called first to obtain the task_id.

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

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

Explicitly states when to use the tool (start/finish/reopen/progress tasks) and when to use an alternative (list_tasks if only task title or assignee is known). Also provides platform-specific notes about ID formats.

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 fully discloses behavior: returns one row per employee including zero hours, week start/end dates, and parameter default handling. No contradictions.

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

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

Four sentences, front-loaded with purpose, each sentence adds value (purpose, usage, parameter, return structure). 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?

Describes return fields (week dates, per-employee rows with utilization_pct) without output schema. Missing explicit output structure (e.g., array), but adequate for low complexity.

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

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

Only parameter 'week' has 0% schema coverage, but description explains ISO week string format and default behavior (current week if omitted), adding critical meaning beyond schema.

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

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

Description clearly states 'Per-employee utilization for one week' with specific metrics (logged hours, capacity, utilization_pct), distinguishing it from siblings like get_project_hours.

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

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

Provides explicit use case examples ('who is over or under capacity') and parameter guidance (ISO week format, omit for current week), but does not explicitly exclude alternatives or mention when not to use.

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