TaScan

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

Annotations already indicate this is a mutation (readOnlyHint=false) and not idempotent. The description adds that it 'Supports bulk creation' but does not disclose side effects, return values, or potential error states. Since there is no output schema, the description could have carried more weight for behavioral transparency, but it remains adequate without contradicting annotations.

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

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

The description is concise and well-structured: a clear main purpose, a critical parameter warning in caps, and a practical note about an alternative tool. Every sentence earns its place with no fluff, and the most important guidance is front-loaded.

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

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

Given the tool's complexity (nested tasks array, critical response_type decisions) and lack of output schema, the description covers the essential usage guidance, including bulk creation and the response_type semantics. It also points to an important alternative, making it sufficiently complete for an agent to select and invoke 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?

Schema coverage is 100%, so the baseline is 3. The tool description adds extra value by giving concrete examples for response_type ('text' for info collection, 'photo' for visual verification, 'checkbox' for simple confirmations), which reinforces and clarifies the schema's already-rich enum descriptions. This enhances parameter understanding beyond the schema.

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

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

The description clearly states the tool's function: 'Add one or more tasks to an event (task list)' with a specific verb and resource, and supports bulk creation. It distinguishes itself from sibling tools by focusing on adding tasks, not creating events or managing other entities.

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 provides a when-not-to-use scenario: 'To dispatch tasks to the Claude Code agent running on Mike's PC, use tascan_dispatch_to_agent instead — it routes directly to the agent's inbox with zero configuration needed.' This names an alternative and gives clear guidance, fulfilling the highest bar.

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?

Annotations already indicate idempotent, non-destructive, and readOnlyHint false. The description adds valuable context by disclosing that server_side_ai triggers a POST to the Anthropic API, explaining why the tool is not purely read-only and highlighting potential external side effects. No contradiction with annotations exists.

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 contribute useful information: protocol context, retrieval details, and the server-side AI capability. It is efficient, though the protocol framing at the start delays the core verb; overall it is appropriately sized and structured.

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

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

With no output schema, the description gives a reasonable summary of the returned data (worker info, message thread, project history, similar issues) and explains the behavior for both default and server_side_ai modes. It covers the tool's complexity well, though exact response formatting is not detailed.

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 baseline is 3. The description reinforces the server_side_ai parameter's behavior (raw data vs. direct AI analysis) but does not introduce new semantic meaning beyond what the schema already provides.

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

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

The description explicitly states the tool retrieves full issue context, listing specific data types (worker info, message thread, project history, similar issues), and also mentions the server-side AI analysis option. It clearly distinguishes itself from sibling tools like list_issues or auto_resolve by framing it as Step 1 of the Closed-Loop Autonomous Operations Protocol.

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

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

The description provides usage context by labeling it as 'Step 1 of the Closed-Loop Autonomous Operations Protocol' and states its intended purpose ('Use this data to reason about the root cause and generate a remediation plan'). However, it does not explicitly mention alternatives or when not to use this tool, so it lacks 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?

Annotations already indicate this is a non-readOnly, non-idempotent, non-destructive operation. The description adds that it 'adds all template tasks', confirming an additive effect, but it does not disclose potential duplication on re-application or error behavior. Little is added beyond the annotations.

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

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

The description is a single, concise sentence that is front-loaded with the verb and clearly states the action and target. No unnecessary words or repetitions.

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 two-parameter mutation with annotations and full schema coverage, the description is mostly complete. It clearly communicates the core functionality but could add context about whether existing tasks are preserved or if applying twice duplicates tasks. However, the non-destructive hint partially covers this.

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?

Both parameters are fully described in the schema (list_id and template_slug) with examples in the template_slug description. The tool description reinforces their role but does not add additional semantic meaning beyond what the schema already provides. Baseline of 3 is appropriate 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 clearly states the action 'apply' with a specific resource 'pre-built template to a task list' and the outcome 'adding all template tasks'. This distinguishes it from sibling tools like tascan_add_tasks, which focus on adding tasks individually rather than through a template.

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

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

The description implies the tool is used when a template_slug is available, but it does not explicitly state when to use this tool over alternatives (e.g., tascan_add_tasks) or mention any prerequisites like listing templates first. Usage is inferred but not directly guided.

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

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

The description adds behavioral context beyond the annotations by detailing the server-side AI analysis, creation of a task list, and dispatch to a worker, including physical-world instruction delivery. This complements the annotations (readOnlyHint=false, destructiveHint=false) and provides a clear picture of the multi-step side effects without contradicting them.

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 and not overly long, but it contains marketing-like phrasing such as 'FULL Closed-Loop Autonomous Operations Protocol' and a patent claim reference that may be unnecessary for practical understanding. The core behavior is present but could be stated more directly 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?

There is no output schema, yet the description does not explain what the tool returns or the outcome after dispatch, such as a confirmation, task list, or status update. It also omits prerequisites like the issue being in a resolvable state, leaving the agent without a full understanding of result handling.

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 issue_id with 100% description coverage, so the schema documents the parameter adequately. The description does not add extra meaning about the parameter format, source, or constraints, leaving the baseline score of 3 appropriate.

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

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

The description clearly indicates the tool performs a full autonomous resolution workflow: analyzing the issue, generating remediation tasks, creating a task list, and dispatching to a worker. This goes beyond simply saying 'auto-resolve' and distinguishes it from sibling tools like tascan_analyze_issue or tascan_add_tasks by emphasizing the complete closed-loop behavior.

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

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

The description implies usage when full autonomous resolution without human intervention is desired, but it does not explicitly state when to use this tool versus alternatives. It lacks exclusions or a direct comparison with sibling tools, such as suggesting tascan_analyze_issue for only analysis or tascan_dispatch_instruction for only dispatch.

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

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

The description adds behavioral context beyond the annotations, noting that it 'Inserts a completion record and timer event.' This informs the agent about side effects and persistence, which is especially valuable since annotations only provide booleans like readOnlyHint=false and destructiveHint=false. No contradiction with annotations.

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

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

The description is two sentences, front-loaded with the primary action, followed by side effects and usage. Every sentence contributes value without redundancy or 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?

Given the tool's simplicity and lack of output schema, the description covers purpose, side effects, and usage adequately. It could mention prerequisites (e.g., task must exist) or return behavior, but these are not essential for a task completion tool with well-defined parameters.

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 baseline is 3. The description does not add significant parameter semantics beyond what the schema already explains; it only reaffirms the role of worker_id ('on behalf of a worker') and hints at response_value via 'simulate,' but these are already covered.

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 what the tool does: 'Complete a task on behalf of a worker' and specifically mentions the side effects: 'Inserts a completion record and timer event.' This distinguishes it from siblings like tascan_update_task or tascan_create_event.

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: 'Use this to simulate or record task completions via the API.' It does not explicitly mention when not to use or list alternatives, but the use case is clear enough to differentiate from updating or deleting tasks.

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?

Annotations already indicate readOnlyHint=false, idempotentHint=false, and destructiveHint=false, so the description does not need to repeat these. The description adds valuable behavioral context beyond annotations by explaining team_mode ('shared completions') and multi_instance ('each worker gets isolated copy'), which describe how the created event behaves for users. It also discloses the mutual exclusion constraint. This enriches the agent's understanding of side effects and semantics.

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 highly concise: two sentences, with the first stating the core purpose and the second covering optional modes and a constraint. It front-loads the essential information and includes no filler or redundant content. 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 the tool's moderate complexity (5 parameters, 2 required, no output schema), the description covers the primary purpose, explains the key boolean modes, and notes a critical constraint. It is missing some details like what the response contains or how the event is structured, but the schema covers parameter definitions, and the annotations cover safety traits. This is nearly complete 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 100%, so all parameters have descriptions. The tool description goes beyond the schema by elaborating on team_mode and multi_instance semantics, explicitly stating their meaning and the rule that they cannot both be true. This adds significant value for the boolean parameters. Other parameters (name, project_id, description) are already well-described in the schema, so no extra detail is needed.

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

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

The description clearly states the tool's purpose: 'Create a new event (task list) within a project.' It specifies the verb (create), resource (event/task list), and scope (within a project). This distinguishes it from sibling tools like tascan_update_event and tascan_delete_event, which have different verbs. The mention of team_mode and multi_instance also clarifies the tool's distinct capabilities.

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: it is for creating new events, and it explains the two key modes (team_mode, multi_instance) and their typical use cases ('great for surveys, onboarding, info collection'). It also states a constraint ('cannot both be true') but does not explicitly compare against alternatives or state when not to use it. Thus, it has clear context but lacks explicit 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?

Annotations already indicate a non-read-only, non-destructive, non-idempotent operation. Description adds no additional behavioral context such as permissions, uniqueness constraints, or return behavior. It merely restates the create action.

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

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

One concise sentence, front-loaded with the action and resource; 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?

For a simple create operation with complete schema and annotations, the description provides enough context by explaining the project is a top-level container for events. It does not mention return values, but the absence of an output schema lowers the requirement.

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 provides 100% coverage with descriptions for 'name' and 'location'. Tool description adds no parameter detail beyond the schema.

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

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

Description uses specific verb 'Create' with resource 'TaScan project' and clarifies its role as 'top-level container for events', distinguishing it from sibling tools like tascan_create_event.

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?

Description does not explicitly state when to use this tool over siblings; it only implies usage via the project concept. No alternative tools or exclusions are mentioned.

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

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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the agent knows this is a non-destructive write. The description adds the 'taskee' alias and organizational scope, but does not disclose any additional behavioral traits such as validation rules, uniqueness constraints, or required permissions. It is consistent with annotations.

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

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

The description is a single focused sentence with no filler, front-loading the operation ('Create') and resource ('worker'). It 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?

While the schema is well-documented and annotations cover the safety profile, the description lacks information about return values (no output schema) or any preconditions. For a simple create operation, this is a minor gap, but it's not fully complete.

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

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

Schema description coverage is 100% (name, email, phone each have descriptions). The description adds no additional meaning beyond the schema, which meets the baseline for high 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 uses the specific verb 'create' and resource 'worker' (with alias 'taskee'), clearly distinguishing from sibling tools like tascan_update_worker and tascan_list_workers.

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 states what the tool does but gives no explicit guidance on when to use it vs alternatives (e.g., tascan_register_agent may be an alternative for adding agents, or tascan_list_workers to check existing). Usage is implied by the action 'create' but no exclusions are provided.

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

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

Annotations already declare destructiveHint=true and readOnlyHint=false. The description adds critical context: the cascade deletion of tasks/completions and irreversibility, which are not inferable from annotations alone. It doesn't contradict annotations.

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

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

Two sentences with no redundancy. Every phrase adds value: the resource, cascade scope, and irreversibility.

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 simple one-parameter schema and no output schema, the description adequately covers purpose, effect, and irreversible nature. It is complete enough for an agent to select and invoke 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 only parameter list_id is fully described in the schema as 'Task list (event) ID to delete' (100% coverage). The description does not add additional parameter-level details, so baseline 3 applies.

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

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

The description clearly states the action (delete), the resource (event/task list), and the scope (all tasks and completions), distinguishing it from sibling delete tools for tasks and projects.

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 lacks explicit 'when to use' guidance or named alternatives, but the scope 'all its tasks and completions' implies a cascading delete, differentiating it from tascan_delete_task. It provides clear context but no exclusions or alternatives.

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?

Annotations already mark this as destructive, but the description adds crucial behavioral details: it cascades to events, tasks, and completions, and is irreversible. This goes beyond the structured hints and informs the agent about the full impact of the operation.

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

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

The description is two concise sentences. The first states the action and scope, the second emphasizes irreversibility. Every word adds value, with no filler or redundancy.

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

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

For a simple delete tool with one parameter, the description adequately covers the behavior (cascade deletion) and consequence (irreversible). No output schema exists, but delete operations typically return simple success indicators. The description is complete given the tool's 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?

The input schema already provides 100% coverage for the single parameter project_id with a description 'Project ID to delete'. The tool description adds no additional semantic detail beyond what the schema provides, 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 clearly states the action (delete), the resource (a project), and the scope (all its events, tasks, and completions). It distinguishes itself from sibling delete tools like tascan_delete_event and tascan_delete_task by specifying the project-level cascade.

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: when removing an entire project and its associated data. It does not explicitly name alternatives or state when not to use it, but the scope is unambiguous and the context is sufficient for an agent to select it appropriately.

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?

Annotations already declare destructiveHint=true and readOnlyHint=false. The description adds value beyond annotations by explicitly noting the cascade effect ('and its completions') and the irreversible nature of the operation, which is not fully captured by the annotations alone.

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

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

The description is two short sentences that immediately state the primary action and a key consequence. Every word is purposeful, with 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?

Given the small parameter surface, the lack of an output schema, and the presence of annotations (destructive, idempotent), the description covers the essential functional context. It explains what is deleted and the irreversible nature, which is sufficient for an agent to invoke it correctly. It could mention edge cases like non-existent tasks, but that is not critical.

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 provides 100% coverage for the single parameter 'task_id' with a clear description. The tool description does not add additional parameter-level context, so the baseline 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 uses a specific verb 'Delete' with a clear resource 'a specific task and its completions', which distinguishes it from sibling delete tools for events and projects. The additional note that the action is irreversible further clarifies the scope.

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 implicitly indicates when to use this tool (when deleting a task) but does not explicitly contrast with alternatives like deleting events or projects. No exclusions or prerequisites are mentioned, so usage guidance is minimal but not misleading.

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

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

The description discloses the tool's multi-channel effects (thread message, notification, feed update, SMS, task creation), which goes beyond the annotations that only indicate non-read-only and non-destructive. This gives the agent critical context about side effects such as SMS delivery and optional task creation, enhancing 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 two sentences, front-loaded with the purpose and then detailing the specific channels. Each element earns its place; the channel list is necessary and concise. 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 7-parameter dispatch tool with no output schema, the description covers the main behavior and side effects. It lacks information about return values or error conditions, but these are not typically required in a tool description. The protocol context adds completeness, though it could explicitly mention what happens after dispatch.

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?

All 7 parameters have descriptions in the schema (100% coverage), so the baseline is met. The tool description adds context about remediation_tasks ('optional remediation task list creation') and send_sms ('SMS if phone on file'), but these are also covered in the schema descriptions. The description does not significantly clarify parameter formats or relationships beyond the schema.

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

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

The description clearly states the tool dispatches remediation to a worker via multi-channel delivery, with a specific verb and resource. It distinguishes from sibling tools like tascan_dispatch_to_agent by explicitly specifying 'to the worker' and listing concrete delivery channels.

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 context as 'Step 3 of the Closed-Loop Autonomous Operations Protocol,' implying it is used after analysis and recommendation, but it does not explicitly state when to use it over alternatives like tascan_dispatch_to_agent or tascan_send_task_email. No exclusions or alternatives are mentioned, so guidance is implied rather than explicit.

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

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

The description adds valuable behavioral context beyond the annotations: tasks are 'picked up and executed automatically', routing prefixes are supported, and the default agent is claude-code-local. It also clarifies that no list ID is needed, which is useful. It does not contradict the annotations (readOnlyHint false, non-destructive).

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 compact and front-loaded with the tool's primary purpose, using only a few sentences. Each sentence provides actionable information (routing prefixes, agent targeting, no list ID). No fluff.

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

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

Given the tool's simplicity and 100% schema coverage, the description covers purpose, routing, and agent selection. It omits return value details, but there is no output schema and execution results are implicitly covered by 'executed automatically'.

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

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

The schema already provides 100% coverage for all three parameters, so the baseline is 3. The description adds some context for the `agent` param by confirming the default and prompting discovery via tascan_list_agents, but it doesn't meaningfully enhance parameter semantics 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 is the 'PREFERRED tool for sending work to an AI agent' and describes dispatching a task to the agent's inbox, distinguishing it from sibling tools like tascan_dispatch_instruction. The verb 'dispatches' and resource 'task to agent's inbox' make the action 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?

It declares itself as the 'PREFERRED tool' and explains when to use it, along with the note that no list ID is needed, differentiating it from other task tools. It also instructs to use tascan_list_agents to discover available agents, providing a clear usage path. However, it doesn't explicitly mention alternatives to avoid, so it falls 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?

Annotations already declare idempotentHint=true, destructiveHint=false, and readOnlyHint=false. The description adds the worker-scanning context but does not disclose output format or side effects beyond generating a QR code. It adds some value without contradicting annotations.

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

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

A single sentence with no unnecessary words. The key information is front-loaded: action, resource, and purpose.

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 low complexity (one param, no output schema), the description adequately covers purpose and usage. It does not explain the return value, but the lack of an output schema makes that omission less critical.

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% for the sole parameter list_id, which the description echoes ('task list (event)'). The description does not add syntax or format details beyond the schema, so the baseline of 3 applies.

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 specifies the action 'Generate' and the resource 'QR code for a task list (event)', with the purpose 'workers can scan to access tasks'. This distinguishes it from sibling tools, as no other tool mentions QR codes.

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

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

The description implies when to use it: when workers need to access tasks via a scannable QR code. It does not explicitly name alternatives or exclusions, but the context of physical scanning is clear enough relative to siblings like tascan_send_task_email.

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?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context that an 'event' is a 'task list' and that the response includes its tasks. This goes beyond the annotations to clarify the response scope.

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

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

The description is a single clear sentence that is front-loaded with the action and resource, and it includes the key behavioral note about including tasks. There is zero redundant content.

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

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

For a simple read-only operation with one fully documented parameter, rich annotations, and no output schema, the description sufficiently indicates what the tool returns (details plus tasks). No additional context is needed to use it effectively.

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 list_id is fully documented in the schema ('Task list (event) ID') with 100% coverage. The description only reiterates the 'event/task list' relationship without adding syntax, format, or parameter-specific details beyond the schema.

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

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

The description clearly states the verb ('Get'), the resource ('specific event'), and adds the crucial parenthetical '(task list)' to clarify terminology. It also notes 'including its tasks', which distinguishes it from siblings like tascan_list_events (listing many events) and tascan_get_task (fetching a single task).

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

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

The phrase 'specific event' implies this is for fetching one event by ID, but there is no explicit guidance on when to use this versus tascan_list_events for enumeration or tascan_get_task for a task's details. Usage context is implied, not spelled out.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds no extra behavioral context beyond restating the read-only nature. No contradictions with annotations.

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

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

The description is a single concise sentence with no filler words. It directly states the tool's purpose without redundant information.

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

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

For a simple get tool with one parameter and rich annotations, the description is adequate. It does not explicitly define what 'details' includes (e.g., whether it returns tasks within the project), but the low complexity and clear scope make this an acceptable gap.

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% with the 'project_id' parameter clearly described as 'Project ID'. The description does not add additional meaning beyond the schema, so it relies on the structured definition.

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 ('Get') and resource ('project'), and clearly scopes to a single project via the project_id parameter. It distinguishes from sibling tools like tascan_list_projects (list all) and tascan_get_task (different resource).

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 usage is implied by the tool name and parameter: use when you need details for a specific project. However, there is no explicit guidance about when to prefer this over tascan_list_projects or any alternatives, nor any 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, providing the safety profile. The description adds useful context about the report contents (task status, completions, workers, photos) beyond the annotations, but doesn't disclose return format, pagination, or error behavior. This is acceptable given the simple nature and annotation coverage.

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

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

The description is a single sentence, front-loaded with the action and resource, and omits all filler. Every word contributes meaning.

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

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

The tool is simple with one parameter and no output schema. The description adequately summarizes the return contents and its relationship to the task list. It lacks explicit edge-case handling (e.g., not found) or return structure details, but given the good annotations and schema, it is sufficiently complete for its complexity.

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

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

Schema coverage is 100% for the single parameter list_id, which includes a description. The tool description adds no extra semantic information about the parameter—it merely repeats the task list/event relationship. Baseline of 3 applies.

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 'Get' and a specific resource 'completion report for a task list (event)', and lists the report contents (task status, completions, workers, photos). This clearly distinguishes it from siblings like tascan_get_event or tascan_get_task, which retrieve different resources.

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

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

The description implies usage when a completion report is needed but does not explicitly compare to sibling tools or state when not to use it. There are no exclusions or alternative tool mentions, so guidance is limited to what the tool does rather than when to prefer 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?

Annotations already declare the tool as read-only, idempotent, and non-destructive, covering the safety profile. The description adds the behavioral detail of returning completions, but it does not disclose error behavior, auth requirements, or pagination. Since annotations carry the main safety burden, a score of 3 is appropriate.

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

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

The description is a single, front-loaded sentence that immediately states the action and resource. There is no redundancy or filler, earning a perfect score for conciseness.

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 get-by-ID tool with one parameter and no output schema, the description is reasonably complete. It indicates the task details and completions will be returned, but does not mention potential error responses or the exact structure of the output. Given the simplicity, this is a minor gap, hence a 4.

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 describes task_id as 'Task ID' with 100% coverage, so the description does not need to add parameter details. The word 'specific' in the description aligns with the single required ID parameter but adds no extra semantic value beyond the schema.

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

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

The description clearly states the action ('Get details') and the specific resource ('a specific task'), distinguishing it from sibling tools like tascan_list_tasks. The addition of 'including completions' clarifies the scope of the retrieved data, 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 implies this tool is for retrieving details of a single task by ID, but it does not explicitly state when to prefer it over alternatives like tascan_list_tasks, nor does it mention any exclusions or prerequisites. The usage context is clear but not elaborated.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which tells the agent this is a safe read operation. The description adds that it lists 'all' agents and includes specific fields (capabilities, inbox IDs, status), but does not disclose additional behavioral traits such as pagination, ordering, or authentication requirements. With annotations covering the safety profile, the description adds some value but not extensive contextual detail.

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 only two sentences. The first sentence states the action and output; the second provides a memorable metaphor and usage context. Every clause earns its place; there is no redundant 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 zero-parameter, read-only list tool with no output schema, the description sufficiently covers what the tool returns (capabilities, inbox IDs, status) and its intended use (discovering agents before dispatching). With annotations covering safety and idempotency, the combined metadata is complete enough for an agent to invoke this tool correctly.

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

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

The tool has zero parameters, and the input schema is empty. The description reinforces the lack of filters by saying 'all registered agents,' which aligns with the schema. Since 0 parameters earns a baseline of 4 and there is nothing else to document, this score is appropriate.

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

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

The description opens with a clear verb+resource: 'List all registered AI agents' and specifies the output fields ('capabilities, inbox IDs, and status'), distinguishing it from sibling list tools like tascan_list_tasks or tascan_list_workers.

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 context for when to use the tool: 'before dispatching work' (supported by the second sentence). It does not explicitly exclude alternatives or name alternate tools, so it stops short of a 5, but the usage context is 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context that events are 'task lists' and that they are scoped to a project, but does not disclose pagination, ordering, or return-format behavior. No contradiction with annotations.

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

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

The description is a single, clear sentence that front-loads the verb and resource. Every word contributes meaning, with no filler or redundancy.

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

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

For a simple list operation with one required parameter and strong safety annotations, the description adequately conveys purpose and scope. It does not specify return fields, but no output schema exists and the tool's behavior is sufficiently implied for an AI agent.

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 fully documents the single parameter project_id with 100% coverage. The description adds no additional parameter semantics beyond the implicit project scoping, 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 uses a specific verb ('List') with a clear resource ('events (task lists)') and scope ('within a project'), making the tool's purpose immediately understandable. It also distinguishes from the singular tascan_get_event and other list tools by explicitly stating it lists all events.

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 use case is implied: use this tool to list all events in a project. However, there is no explicit guidance on when to prefer this over alternatives like tascan_get_event or tascan_list_tasks, and no when-not-to-use conditions are stated.

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?

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds behavioral context beyond this by stating the scope ('all issues'), the statuses included (open, acknowledged, resolved), and the fields returned (severity, type, category). This is meaningful, though it does not cover pagination or authorization.

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

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

The description is two sentences, front-loaded with the action and resource, followed by a useful downstream-use note. Every sentence earns its place with no redundancy or 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 simple one-parameter read-only tool with rich annotations, the description fully covers what the tool does, what it returns, and why it is used (to feed into tascan_analyze_issue). It is well-suited to guide an AI agent in selecting and invoking the tool.

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

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

Schema coverage is 100%, with list_id described as 'Task list (event) ID'. The description repeats this term but does not add further parameter-level details. Therefore, the schema carries the parameter semantics, and the description adds no extra value beyond the 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?

The description uses a specific verb-resource pair ('List all issues for a task list (event)') and clearly distinguishes from siblings like tascan_list_tasks (tasks vs issues) and tascan_list_events (events only). It further specifies the returned data (open, acknowledged, resolved issues with severity, type, category), making the purpose unmistakable.

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 a clear usage context: 'Use this to discover issues that need AI analysis via tascan_analyze_issue.' This implicitly guides the AI to use this tool before analysis. It lacks explicit exclusions or alternatives, but the intended workflow 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds organizational scope ('in the organization') but does not disclose behaviors like pagination, ordering, or return format. This adds some value beyond annotations but not rich detail.

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

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

The description is a single concise sentence with no filler or redundancy. It states the verb, resource, and scope efficiently, earning every word.

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

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

For a zero-parameter list tool with strong annotations (read-only, idempotent, non-destructive), the description is largely complete. It clarifies scope ('all ... in the organization') and implies the return of a list of projects. Without an output schema, it could specify return value structure, but the low complexity makes this sufficient.

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

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

The tool has zero parameters, so there is nothing about parameters that needs explanation. The baseline for 0 params is 4, and the description makes this explicit by stating it lists all projects without filters.

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

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

The description uses the specific verb 'List' and names the exact resource 'TaScan projects' with an explicit scope ('in the organization'). This clearly distinguishes it from sibling tools like tascan_get_project (single project) and tascan_create_project.

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: use this tool when you need all TaScan projects in the organization. However, it does not explicitly state when not to use it or mention alternatives such as tascan_get_project for retrieving a single project.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the event/task list scoping, but doesn't disclose any further behavioral details such as output shape or pagination. With annotations present, the added value is modest.

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

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

The description is a single, front-loaded sentence that states the action and scope with no filler. Every word earns its place.

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

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

For a simple list tool with one well-documented parameter and strong annotations, the description is largely complete. It clearly identifies the target resource and scope, though it might ideally mention that it returns a list of task records. No output schema exists, so the description carries that burden, but the intent is unambiguous.

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 fully documents list_id as 'Task list (event) ID' with 100% coverage. The tool description merely echoes this relationship ('in an event (task list)') and adds no new semantic information 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 'List all tasks in an event (task list)' uses a specific verb (List) and resource (tasks in an event/task list), clearly distinguishing it from sibling tools like list_events or get_task. The parenthetical clarifies the scoping.

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 you need all tasks within a specific event/task list), but it doesn't explicitly state alternatives or situations where another tool should be used. This is clear context without 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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds little beyond confirming it lists templates; it does not disclose additional behavioral traits like pagination, ordering, or result format, which is acceptable given the annotations.

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

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

The description is a single sentence that is concise, front-loaded with the action, and contains no redundant information. Every word adds value.

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

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

For a simple list tool with no output schema and only one optional parameter, the description provides sufficient context. It clarifies that both built-in and saved templates are included, which covers the key aspect of what the user receives.

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 only parameter 'category' is fully described in the schema with a description and example values (100% schema coverage). The tool description adds no additional parameter semantics, 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 clearly states the verb 'List' and the resource 'task templates', specifying scope as 'built-in and saved'. This distinguishes it from sibling tools like tascan_list_tasks or tascan_apply_template, which serve different resources or actions.

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 retrieving templates but does not explicitly state when to use this tool over alternatives or provide exclusions. There is no mention of alternatives such as tascan_apply_template, leaving usage guidance implied rather than explicit.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds the organizational scope ('all... in the organization'), which is useful behavioral context. However, it does not disclose pagination, ordering, or filtering limitations. With annotations present, this is adequate but not rich.

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

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

The description is a single, front-loaded sentence with no filler. It effectively communicates the action and resource.

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 zero-parameter schema and the annotations covering safety/idempotency, the description fully suffices for an agent to understand and invoke the tool. It clearly defines the outcome without needing extra details.

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

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

The tool has zero parameters, and the input schema is empty. Per the rubric, a baseline of 4 is appropriate since there are no parameter semantics to explain. The description does not need to add anything.

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

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

The description clearly states the action ('List') and the resource ('workers'), with scope ('all in the organization'). The parenthetical '(taskees)' clarifies terminology, and it distinguishes from siblings like tascan_list_agents.

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

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

The description implies when to use the tool (whenever a list of all workers is needed), but it does not explicitly contrast with alternative tools such as tascan_list_agents or mention prerequisites. For a simple list operation, context is understood but no explicit guidance is provided.

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

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

Annotations are generic (readOnly=false, destructive=false), so the description adds meaningful behavioral context: the recommendation is 'persisted in the AI audit trail' and the tool 'accepts' both text and structured recommendations for auto-dispatch. This clarifies the write side-effect and audit trail integration beyond what annotations state. No contradiction found.

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 compact—three sentences—with the core action and protocol step presented first. Every sentence earns its place: action, parameter flexibility, and persistence behavior. No fluff or redundancy.

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

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

For a write-only tool with a nested structured parameter and no output schema, the description sufficiently covers the purpose, optional vs required inputs, and the audit-trail persistence. It could mention response behavior, but the absence of an output schema lowers that burden. The protocol step context further helps the agent decide when to invoke.

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%, giving the baseline of 3. The description adds value by explaining that structured_recommendation contains 'task definitions for auto-dispatch,' which clarifies the semantic purpose of that complex nested parameter beyond the raw format in the schema. This extra context elevates the score.

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 ('Post') and identifies the exact resource ('AI-generated recommendation to an issue thread'), naming it as 'Step 2 of the Closed-Loop Autonomous Operations Protocol.' This makes the tool's role clear and distinguishes it from sibling tools like tascan_analyze_issue or tascan_auto_resolve by focusing on issuing a recommendation rather than analyzing or directly executing.

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 statement 'Step 2 of the Closed-Loop Autonomous Operations Protocol' provides clear contextual when-to-use guidance, implying it follows issue analysis and precedes auto-dispatch. However, it does not explicitly state when not to use this tool or name alternative tools, leaving minor ambiguity.

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?

Annotations already cover idempotency and non-destructive behavior. The description adds useful behavioral context: the registered agent will appear in tascan_list_agents and can receive dispatched tasks. It does not disclose edge cases like duplicate ID handling or auth requirements, but supplements the annotations well.

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

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

The description is two concise sentences that front-load the primary action and immediately provide key behavioral outcomes. Every word earns its place, with no unnecessary 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?

The description covers the main purpose and post-registration effects, and the schema fully documents all parameters. It omits error/edge cases such as duplicate IDs or permissions, but given the tool's moderate complexity and good annotations, it is sufficiently complete for effective use.

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%, meaning all 9 parameters are fully documented in the input schema. The description adds no additional parameter-level meaning, so a 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 clearly states the tool's action: registering a new AI agent in the agent registry. It further distinguishes itself by noting the agent will appear in tascan_list_agents and can receive dispatched tasks, which is specific and unique among siblings.

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: 'Self-registration for AI agents joining the TaScan network' implies when this tool should be used. However, it does not explicitly exclude alternatives like tascan_create_worker or mention scenarios where this tool should not be used, so it stops 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?

Annotations already establish that this tool has open-world side effects and is non-idempotent (readOnlyHint=false, openWorldHint=true, idempotentHint=false). The description adds useful behavioral context by revealing the email is sent through SendGrid and includes specific visual components, though it does not mention potential delivery failures or rate limits. No contradiction with annotations.

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

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

The description is two sentences long, front-loaded with the core action and provider. Every sentence earns its place, with no filler or redundant restatement of the tool name.

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 an email-sending tool with 7 parameters and no output schema, the description sufficiently covers purpose, target, and content. It lacks explicit mention of success/failure response or idempotency implications, but these are either covered by annotations or implied by 'send email.' Overall, it is adequate for selection and invocation.

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

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

Schema description coverage is 100%, so parameter semantics are fully documented in the schema. The description mentions QR code inclusion, which maps to include_qr, but does not add meaningful meaning beyond what the schema already provides. The only added context is the overall email composition.

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 the specific action 'Send a branded TaScan task notification email via SendGrid,' clearly identifying both the verb and resource. It distinguishes this tool from its siblings by being the only email-sending tool, and further specifies the email contents (QR code, task summary, Open button).

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 indicates it can 'notify anyone about a specific task list or task,' providing some usage context. However, it does not explicitly state when to prefer this tool over alternatives like dispatch_to_agent or generate_qr, nor does it include exclusion conditions.

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

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

The description adds the key constraint that 'team_mode and multi_instance cannot both be true,' which is not present in annotations. It also explicitly lists the modifiable fields. Given that annotations already declare idempotentHint=true and destructiveHint=false, the description provides useful additional context about the update behavior and validation rules.

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

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

The description is a single sentence plus a brief constraint, with no filler. It front-loads the primary purpose and lists fields efficiently, earning its place with every word.

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 an update tool with 6 parameters and no output schema, the description covers the resource, updatable fields, and a critical validation rule. It doesn't mention behavior when list_id doesn't exist or partial update semantics, but given the annotations, it is reasonably complete for the tool's complexity.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by stating the mutual exclusivity of team_mode and multi_instance, which is an inter-parameter relationship not captured in individual property descriptions. It also reiterates the field list, reinforcing what can be updated.

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 'Update an event / task list' with a specific verb and resource, and lists the updatable fields (name, description, team_mode, multi_instance, timer_mode). This distinguishes it from sibling create/delete/get tools, though it doesn't explicitly differentiate from other update 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?

The 'Update' verb implies this tool is for modifying existing events, but the description does not explicitly state when to use it versus alternatives like tascan_create_event or tascan_get_event. The constraint about team_mode and multi_instance is a validation rule, not usage guidance, so the guidance is implied rather than explicit.

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

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

Annotations already provide idempotentHint=true, destructiveHint=false, and readOnlyHint=false, so the safety profile is known. However, the description adds no extra behavioral context (e.g., partial update semantics, whether missing fields retain their values, or return behavior).

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

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

The description is a single sentence with no filler, front-loading the verb and resource. Every word adds value, making it highly concise.

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?

Despite comprehensive schema coverage, the description omits important usage context such as whether only provided fields are updated, how it behaves relative to create_project, and what the tool returns (no output schema). Annotations help but do not fully compensate for the lack of behavioral guidance in a mutation tool.

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

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

The input schema has 100% coverage with meaningful descriptions for all six parameters. The description lists a subset of these fields but does not add new information beyond what the schema already provides, so the baseline 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 clearly identifies the tool as updating a project and lists the updatable fields (name, location, status, dates). This distinguishes it from sibling update tools like update_task or update_event by resource type, though it does not explicitly contrast with alternatives such as create_project.

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

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites (e.g., existing project), nor does it differentiate from create_project or delete_project. The usage is only implied by the verb 'update'.

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?

Annotations already declare readOnlyHint=false, destructiveHint=false, and idempotentHint=true, so the agent knows it's a safe, idempotent write. The description adds no behavioral context beyond the field list: it doesn't clarify whether partial updates are allowed, what happens if the task doesn't exist, or whether the update returns the modified task. The description provides no value beyond what annotations already offer.

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

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

The description is a single, concise sentence with the key fields in parentheses. It is front-loaded with the action and resource, containing zero filler or redundancy.

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

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

Given the rich schema (7 params, 100% coverage) and annotations (idempotent, non-destructive), the description is mostly sufficient for invoking the tool. The main gap is not specifying the return value or confirming whether updating is partial, but the absence of an output schema lowers the expectation for return-value details.

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% with each parameter described. The description merely recaps field names without adding new meaning, so it earns the baseline score. It doesn't explain the semantics of 'flags' collectively or provide guidance on how the boolean flags interact with response_type.

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

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

The description clearly states the action ('Update') and the resource ('a task'), with specific fields listed. It distinguishes itself from sibling tools like tascan_add_tasks (creating) and tascan_delete_task (removing) by the verb 'update'.

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?

Usage is implied by the verb 'update'—it's for modifying existing tasks, not adding or deleting them. However, there is no explicit mention of when to use this tool versus alternatives like tascan_complete_task or tascan_add_tasks, and no guidance on prerequisites (e.g., task must exist).

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?

Annotations already declare the mutation (readOnlyHint=false), idempotency (idempotentHint=true), and non-destructive nature (destructiveHint=false). The description adds no extra behavioral context beyond restating the update action, which is consistent. No contradiction found.

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?

A single sentence that is direct and free of redundancy: 'Update a worker profile (name, phone, email)'. Every word contributes to the meaning, and the key fields are front-loaded in parentheses.

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 CRUD update tool with no output schema and no nested objects, the description combined with schema/annotations is largely sufficient. It clearly identifies the resource and the updatable fields. Minor gap: it doesn't mention whether the worker must exist or what the response format is, but these are not critical for an update operation.

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

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

Input schema has 100% parameter coverage with clear descriptions (e.g., 'New name', 'New email', 'Worker ID'). The description merely lists fields that are already documented, adding no additional semantic meaning. Baseline of 3 applies when schema does the heavy lifting.

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 ('Update'), resource ('worker profile'), and lists the fields involved ('name, phone, email'). This clearly distinguishes it from sibling tools like tascan_create_worker or tascan_update_project, which target different resources or actions.

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

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

The description implies usage: when you need to modify a worker's details, use this tool. However, it provides no explicit exclusions or comparisons to alternatives (e.g., when to use create_worker instead of update_worker). The context is clear but minimal.

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