Talent-Augmenting Layer

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

Annotations provide only a title, so description bears full burden. It discloses disk saving as a side effect but doesn't specify overwrite behavior, error conditions, or prerequisites beyond scores. Adequate but could be more precise.

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?

Description is a single paragraph of four sentences, front-loaded with the core action. Slightly verbose in listing parameter groups, but no unnecessary information. Could be more concise but still clear.

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

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

For a tool with 19 parameters and no output schema, description covers the basic workflow and required prerequisite (talent_assess_score), but lacks details on return format, validation, or edge cases. Completeness is adequate but not thorough.

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 schema already explains each parameter. Description groups parameters conceptually (demographic info, goals, etc.) but adds no new meaning beyond the schema. 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?

Description explicitly states it generates and saves a profile from assessment data. It distinguishes from siblings by mentioning it's called after talent_assess_score, and lists inputs and outputs clearly.

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 provides clear guidance to call this after talent_assess_score, establishing a sequential workflow. It implies this is the profile creation step, but doesn't explicitly exclude cases or mention 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?

Description implies a read-only computation with no side effects, but does not explicitly state safety, auth requirements, or potential limitations. With no annotations for behavioral context, this could be more transparent.

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

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

Four concise sentences, each adding unique value: purpose, input/output details, and usage instruction. No wasted words.

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

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

For a computational tool with nested parameters and no output schema, the description fully covers output details, parameter roles, and integration with the profile creation tool. Complete for its purpose.

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%, and description adds value by explaining the structure of answers and domain_ratings with examples, going beyond the schema definitions.

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

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

Description clearly states it computes TAOS assessment scores from numeric answers, specifies inputs and outputs, and distinguishes from sibling tools like talent_assess_create_profile.

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

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

Explicitly says 'Call this once all assessment questions have been answered, then pass the result to talent_assess_create_profile', providing clear when-to-use and next steps.

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?

Describes return value and conversational flow. Annotations provide no behavioral hints, so description carries full burden; it adequately discloses non-destructive nature.

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?

Concise, front-loaded with core purpose, and every sentence adds value. No wasted words.

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

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

Tool is simple with no output schema; description provides all needed info: what it returns, workflow, and when to call.

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

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

Schema coverage is 100%; description does not add extra meaning beyond what schema already says about the optional 'name' parameter.

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

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

Clearly states it starts an assessment and returns the full protocol. Distinct from siblings by specifying the workflow and next steps.

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

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

Explicitly says to call at beginning of onboarding conversation. Describes subsequent tools to use, providing clear usage context.

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

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

Annotations are minimal (only a title), so the description carries full burden. It mentions the return value but does not disclose whether the operation is read-only, requires specific permissions, or has any side effects. Behavioral traits are inadequately described.

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

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

The description is a single, well-structured sentence that front-loads the verb and resource. Every word adds value, and it is appropriately concise without sacrificing clarity.

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

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

No output schema exists, but the description adequately details the return format (five categories with AI behavior). However, it omits prerequisites (e.g., existence of a profile) and error conditions, leaving some gaps for a moderately complex 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%, providing basic parameter descriptions. The description adds meaningful context by explaining that classification depends on the user's profile and specifying the exact return categories, which helps the agent understand parameter intent 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 classifies a task using the user's profile and returns specific categories (automate, augment, coach, protect, hands_off) with AI behavior. It uses a specific verb ('classify') and resource ('task'), making it distinct from sibling tools like talent_assess_score or talent_get_profile.

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

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

The description implies usage for classifying tasks based on a profile, but does not explicitly state when to use it versus alternatives (e.g., talent_assess_score). No guidance on prerequisites or exclusions 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 only provide a title ('Delete Profile'), which aligns with the description but adds no behavioral hints. The description discloses the destructive nature ('Delete') and scope ('profile and interaction logs'), which is valuable context beyond annotations. However, it lacks details like authentication requirements, irreversibility, or confirmation steps.

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 with zero wasted words. It's front-loaded with the core action and target, making it highly efficient and easy to parse.

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 destructive tool with no output schema and minimal annotations, the description adequately covers the basic purpose. However, it lacks details on behavioral implications (e.g., permanence, side effects) and usage context, which would be helpful given the tool's critical nature among read-only and other write siblings.

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% description coverage, with the 'name' parameter documented as 'User's name'. The description doesn't add any parameter-specific semantics beyond what the schema provides, so it meets the baseline for high 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 ('Delete') and the target ('a user's profile and interaction logs'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'talent_get_profile' or 'talent_save_profile' beyond the obvious destructive nature.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, consequences, or when not to use it (e.g., versus archiving or updating). The agent must infer usage from the name and description alone.

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

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

With no annotations beyond a title, the description bears full burden for behavioral disclosure. It accurately describes the returned content as a compact JSON block, but does not mention side effects, permissions, rate limits, or that it is a read-only operation (implied by 'Get'). The description adds value but lacks depth on behavioral traits.

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 extremely concise—only two sentences. The first sentence states the core purpose, and the second explains the return value and its intended use. No extraneous words or redundant information. Information is front-loaded and every sentence is valuable.

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 (single parameter, no output schema), the description sufficiently explains the purpose and return format. It lists the components included in the output, which is helpful. However, it does not specify defaults or optionality of the returned fields, leaving minor gaps. Overall, it is nearly complete for the context.

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 covers 100% of parameters, with one property 'name' described as 'User's name'. The description adds no additional meaning or constraints beyond the schema. Per guidelines, this earns the baseline score of 3.

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 gets 'calibration settings for a user' and lists specific components ('friction levels, coaching domains, red lines, and interaction preferences'), clearly distinguishing it from sibling tools like talent_get_profile which likely return full profiles. The verb 'Get' and resource 'calibration settings' are specific and unambiguous.

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

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

The description implies the tool is for retrieving calibration settings for LLM system prompts, but it does not explicitly state when to use this tool versus alternatives (e.g., 'use this for calibration-specific data, not for full profiles'). No exclusions or when-not-to-use guidance is provided, leaving usage context somewhat ambiguous.

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?

Describes a non-destructive read operation returning full profile data. No contradictory annotation (annotations only have a title). Could add behavior on missing profile or idempotency but is adequate for the tool's simplicity.

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 wasted words: first sentence describes functionality, second gives concrete usage instruction. Ideal front-loading.

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 single-parameter input and lack of output schema, the description covers the return contents adequately. Could mention error handling for missing profiles but is otherwise 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 provides 100% coverage with explicit description of the 'name' parameter including an example. Description adds no additional 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?

Description clearly states loading a profile by name and lists specific returned fields (expertise map, calibration settings, etc.), which distinguishes it from siblings like talent_get_calibration or talent_get_progression.

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

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

Explicitly advises using this tool at the start of every conversation, providing clear contextual guidance. Does not explicitly exclude alternative tools but sufficiently implies primary usage.

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 not provided in the input, so the description carries the full burden. It describes what the tool returns (e.g., interaction counts, engagement patterns, growth/atrophy signals, warnings), which adds behavioral context beyond a basic read operation. However, it lacks details on permissions, rate limits, or error handling, which are important for a tool that might involve sensitive user data. No contradiction with annotations exists, as annotations are absent.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and key outputs. It front-loads the main action ('Get skill progression analysis') and lists specific details without unnecessary words, making it easy for an agent to parse and understand quickly.

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

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

Given the tool has one parameter with full schema coverage and no output schema, the description provides a good overview of what the tool does and returns. However, it lacks information on output format, error cases, or how it integrates with sibling tools, which could be important for an agent to use it correctly in context. The description is adequate but has clear gaps in completeness.

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

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

Schema description coverage is 100%, with the single parameter 'name' fully documented in the schema as 'User's name'. The description does not add any further semantic details about the parameter, such as format constraints or examples. Since the schema handles the parameter documentation adequately, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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

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

The description clearly states the tool's purpose: 'Get skill progression analysis for a user.' It specifies the verb ('Get') and resource ('skill progression analysis'), and the details about what it shows (interaction counts, engagement patterns, etc.) add specificity. However, it doesn't explicitly distinguish this tool from siblings like 'talent_get_profile' or 'talent_get_calibration', which might also retrieve user-related data, so it falls short of a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or compare it to sibling tools such as 'talent_get_profile' or 'talent_assess_score', which could be related. This leaves the agent with minimal direction, relying solely on the tool name and description for inference.

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

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

No annotations beyond a title, so the description must carry the full burden. It does not disclose whether this is read-only, has side effects, requires authentication, or if there are any limits (e.g., pagination). The word 'list' implies read-only but is not explicit.

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?

Single, efficient sentence with no unnecessary words. Perfectly concise for the context.

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

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

Given zero parameters and no output schema, the description is minimally adequate. However, it lacks details about the output (e.g., whether it returns IDs, names, or full objects) and does not help the agent understand how to use the results with sibling tools.

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

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

No parameters exist, and schema description coverage is trivially 100%. The description adds no parameter-specific information, which is acceptable for zero parameters. Baseline of 4 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 verb (List) and resource (Talent-Augmenting OS profiles), distinguishing it from sibling tools like talent_get_profile (single) and talent_delete_profile. However, 'all available' is ambiguous—whether it means all profiles in the system or only those accessible.

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 on when to use this tool vs alternatives such as talent_get_profile for a single profile or talent_save_profile for updates. The description does not mention exclusions or prerequisites.

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 only provide a title ('Log Interaction'), so the description carries the burden of behavioral disclosure. It adds value by specifying the timing ('after substantive AI interactions') and purpose ('track engagement patterns and skill development'), but doesn't cover aspects like permissions, rate limits, or what happens if logging fails. 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?

The description is concise and front-loaded, with two sentences that efficiently convey purpose and usage. Every sentence adds value without redundancy, though it could be slightly more structured by explicitly listing key parameters or outcomes.

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

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

Given no output schema and minimal annotations, the description is adequate but has gaps. It covers the tool's purpose and basic usage but lacks details on return values, error handling, or how this tool integrates with sibling tools for a complete skill-tracking workflow.

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%, with all parameters well-documented in the schema (e.g., enums for 'skill_signal', 'task_category', 'engagement_level'). The description doesn't add meaning beyond the schema, such as explaining how parameters relate to each other or providing examples, so it 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 clearly states the tool's purpose: 'Log an interaction for skill tracking' specifies the verb (log) and resource (interaction), and 'Call this after substantive AI interactions to track the user's engagement patterns and skill development' adds context. However, it doesn't explicitly differentiate from sibling tools like 'talent_assess_score' or 'talent_save_profile', which might also involve logging or assessment.

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 some usage context: 'Call this after substantive AI interactions' implies when to use it. However, it doesn't specify when not to use it or mention alternatives among sibling tools, such as when to use 'talent_assess_score' instead for scoring vs. logging interactions.

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 minimal (only a title), so the description carries the burden of behavioral disclosure. It describes what the tool returns (aggregate metrics, trend alerts, skill breakdown) but lacks details on permissions, rate limits, or data freshness. Since annotations don't cover these aspects, the description adds some value but not rich behavioral context.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by specific details and usage context. Every sentence adds value without waste, making it efficiently structured and appropriately sized for the tool's complexity.

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 (organizational summary with multiple metrics) and lack of output schema, the description does a good job explaining what information is returned. However, it could be more complete by specifying the format of the output (e.g., JSON structure) or any limitations, which would help the agent 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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately focuses on output semantics without redundant parameter info, earning a high baseline score for this dimension.

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

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

The description clearly states the tool's purpose with specific verbs ('Get an organisation-level summary') and resources ('across all profiles'), distinguishing it from sibling tools like talent_get_profile (individual) or talent_list_profiles (listing). It details the content of the summary (aggregate dependency risk, growth potential, etc.), making the purpose explicit and distinct.

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

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

The description provides clear context for usage ('For org dashboards'), indicating it's intended for high-level organizational overviews rather than individual profile analysis. However, it does not explicitly state when not to use it or name alternatives (e.g., talent_get_profile for individual details), which prevents a perfect score.

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 only provide a title ('Parse Telemetry Logs'), so the description carries the burden of behavioral disclosure. It adds valuable context beyond annotations: it describes what the tool does (parses and records telemetry), mentions the system prompt instruction for LLM emission, specifies dual logging destinations (local JSONL and optional hosted API), and implies a read-only extraction process. No contradictions with annotations exist.

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

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

The description is appropriately sized and front-loaded, with three sentences that each add value: the first states the purpose, the second provides usage instructions, and the third explains the outcome. There is no redundant or wasted information, and it efficiently communicates key details.

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

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

Given the tool's moderate complexity (parsing and logging telemetry), no output schema, and annotations limited to title, the description is mostly complete. It covers purpose, usage, and behavioral outcomes, but lacks details on error handling, format of parsed telemetry, or specifics on the optional API push. However, it provides sufficient context for an agent to invoke the 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?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds some semantic context by linking 'name' to 'profile-linked logging' and specifying that 'response_text' should contain <tal_log> blocks, but it does not provide additional details beyond what the schema offers. This meets the baseline for high 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 specific action ('parse telemetry blocks from an LLM response and record them'), identifies the resource (<tal_log> telemetry blocks), and distinguishes it from siblings by focusing on parsing telemetry rather than assessment, classification, or profile management. It explicitly mentions the system prompt context and the dual logging destinations.

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 ('call this tool with the full LLM response text to extract and log all telemetry entries'), including the prerequisite that the LLM response contains <tal_log> blocks. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, though the focus on telemetry parsing implies differentiation from other talent_* tools.

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

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

The description indicates this is a write operation ('save or update'), which aligns with the absence of readOnlyHint in annotations. However, it doesn't add significant behavioral context beyond what's implied by 'save/update'—such as whether it overwrites existing data, requires specific permissions, or handles errors. With no annotations provided, the description carries the burden but offers only basic operational intent.

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 front-loaded, consisting of two sentences that directly state the tool's purpose and usage guidelines. Every sentence adds value without redundancy, making it efficient and well-structured.

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

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

Given the tool's complexity (a write operation with 2 parameters), no annotations, and no output schema, the description is reasonably complete. It covers the purpose, usage context, and implies mutation behavior, but lacks details on return values or error handling. It's adequate for basic use but could be more comprehensive for 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 description does not explain the parameters beyond what the schema already covers (100% schema description coverage). It mentions 'user's profile markdown content' and 'save or update', which loosely relate to the 'name' and 'content' parameters, but adds no additional semantic details like format constraints or usage examples. Baseline 3 is appropriate given the high 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 ('save or update') and resource ('user's profile markdown content'), making the purpose specific and unambiguous. It distinguishes this tool from siblings like 'talent_get_profile' (read) and 'talent_delete_profile' (delete) by emphasizing write operations.

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

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

The description provides explicit guidance on when to use this tool: after running '/talent-assess to write the generated profile' or after '/talent-update to save changes'. This clearly differentiates it from other tools and specifies the workflow context.

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

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

Annotations only provide a title ('Get User Status'), so the description carries the full burden. It describes what information is returned (e.g., trend direction, atrophy warnings) but lacks details on behavioral traits like error handling, response format, or performance characteristics. 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?

The description is front-loaded with the core purpose, followed by specific details and usage guidance in two efficient sentences. Every sentence adds value without redundancy, making it appropriately sized and well-structured.

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

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

Given the tool's complexity (multiple data points) and lack of output schema, the description does well by listing returned components like profile summary and recommended actions. However, it could be more complete by hinting at the response structure or data types, though annotations are minimal and no output schema exists.

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%, with the single parameter 'name' documented as 'User's name'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high 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 specific action ('Get a comprehensive status report') and resource ('for a user'), listing concrete components like profile summary, calibration, and skill progression. It distinguishes from siblings by focusing on a holistic overview rather than specific operations like talent_get_profile or talent_get_calibration.

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 explicitly states when to use this tool ('for a quick overview at the start of a conversation'), providing clear context for its application. This helps differentiate it from more targeted siblings like talent_get_calibration or talent_get_progression.

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 annotations. Annotations only provide a title ('Suggest Expertise Domains'), so the description carries the full burden. It discloses that the tool 'Returns a curated list of domain suggestions with descriptions drawn from an industry-specific taxonomy' and notes 'The LLM has override authority and can add or remove domains from the suggestions,' which clarifies output format and user interaction. No contradictions with annotations are present.

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 in three sentences. The first sentence states the purpose, the second describes the output and usage context, and the third adds important behavioral detail about LLM authority. Each sentence earns its place with no wasted words, and key information 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 moderate complexity (3 parameters, no output schema, no annotations beyond title), the description is mostly complete. It covers purpose, usage context, output behavior, and LLM interaction. However, it lacks details on potential errors, rate limits, or authentication needs, which could be relevant for a tool in a talent assessment system. With no output schema, it could also benefit from more specifics on the return format.

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 input schema already documents all parameters (role, industry, responsibilities) with descriptions. The description adds minimal semantic value beyond the schema by mentioning 'based on their role, industry, and responsibilities,' which merely restates the parameter names without providing additional meaning or usage insights. Baseline 3 is appropriate as the 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 clearly states the tool's purpose: 'Suggest expertise domains for a user based on their role, industry, and responsibilities.' It specifies the verb ('suggest'), resource ('expertise domains'), and input criteria. However, it does not explicitly differentiate from sibling tools like 'talent_classify_task' or 'talent_assess_create_profile', which might have overlapping purposes in the talent assessment context.

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

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

The description provides clear context for usage: 'Use this during the assessment to help identify relevant domains for the Expertise Self-Assessment (ESA).' This indicates when to use the tool (during assessment) and its goal (identifying domains for ESA). However, it does not specify when not to use it or name explicit alternatives among sibling tools, such as when to use 'talent_classify_task' instead.

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