Talent-Augmenting Layer

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

The description adds valuable behavioral context beyond annotations. While annotations only provide a title, the description discloses that the tool 'Returns the generated profile and saves it to disk,' indicating both a return value and a side effect (persistence). However, it doesn't mention potential errors, file formats, or overwrite behavior, leaving some gaps in full 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 extremely concise and well-structured in three sentences. The first sentence states the core purpose, the second provides usage guidelines, and the third clarifies inputs and outputs. Every sentence adds essential information with zero waste, making it easy 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 complexity (19 parameters, nested objects) and lack of output schema, the description is reasonably complete. It covers purpose, prerequisites, inputs, and outputs, but doesn't detail the return format or potential errors. For a tool with rich schema coverage but no output schema, it could benefit from more information about the generated profile structure or error conditions.

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

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

With 100% schema description coverage, the input schema already documents all 19 parameters thoroughly. The description adds minimal parameter semantics by listing categories of data ('computed scores, demographic info, goals, task classifications, and preferences') but doesn't provide additional syntax, format, or usage details beyond what the schema already specifies. 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 tool's purpose: 'Generate and save a complete Talent-Augmenting Layer profile from assessment data.' It specifies the verb ('Generate and save'), the resource ('profile'), and distinguishes it from its sibling 'talent_assess_score' by stating it should be called after that tool. The description also mentions what data it takes and what it returns, making the purpose highly specific.

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

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

The description provides explicit usage guidance: 'Call this after talent_assess_score to create the profile file.' It names the specific prerequisite tool ('talent_assess_score') and indicates the sequential workflow, clearly stating when to use this tool versus alternatives like 'talent_save_profile' or 'talent_get_profile'.

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 ('Compute Assessment Scores'), so the description carries the full burden of behavioral disclosure. It describes the computation process and output details (scores with interpretations and calibration recommendations), which adds value beyond the title. However, it does not mention potential side effects, error conditions, or performance characteristics like rate limits, leaving some behavioral aspects unclear.

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, dense sentence that efficiently conveys the tool's purpose, inputs, and outputs without unnecessary words. It is front-loaded with the core action ('Compute all Talent-Augmenting Layer scores'), but could be slightly more structured for readability, such as by separating input and output details into distinct clauses.

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

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

Given the complexity of computing multiple scores from structured inputs, the description provides a good overview but lacks an output schema, leaving the exact format of the returned scores, interpretations, and recommendations unspecified. With no annotations beyond a title and high schema coverage, the description compensates somewhat by detailing the outputs, but more behavioral context or output examples would enhance 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 clear documentation for both parameters ('answers' and 'domain_ratings'), including examples and value ranges. The description does not add any parameter-specific details beyond what the schema provides, such as explaining the significance of the scores or how they are computed. Thus, it meets the baseline of 3 where 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 specific action ('Compute all Talent-Augmenting Layer scores'), identifies the input resources ('raw assessment answers'), and lists the outputs ('ADR, GP, ALI, ESA, and composite TALRI scores with interpretations and recommended calibration'). It distinguishes this tool from siblings like 'talent_assess_create_profile' or 'talent_get_profile' by focusing on score computation rather than profile management or retrieval.

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 raw assessment data is available, but it does not explicitly state when to use this tool versus alternatives like 'talent_get_calibration' or 'talent_suggest_domains'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool's purpose 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?

The description adds valuable context beyond annotations (which only provide a title), explaining the return format ('full assessment protocol with all questions, behavioural anchors, and instructions'), how the chatbot should use it ('ask questions one at a time, collect answers'), and the subsequent workflow steps. 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 efficiently structured in three sentences: first states the purpose and return value, second explains the workflow, third provides usage timing. Every sentence adds essential information with zero waste.

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

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

Given the tool's complexity (initiating an assessment workflow) and lack of output schema, the description is mostly complete—it explains what it returns and how to use it in context. A minor gap exists in not detailing potential error conditions or authentication needs, but it adequately covers the core functionality.

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

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

With 100% schema description coverage (the single parameter 'name' is fully documented in the schema as optional and for the person being assessed), the description adds no additional parameter information beyond what the schema provides, meeting 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 tool's purpose with specific verbs ('Start a Talent-Augmenting Layer onboarding assessment') and resources ('assessment protocol with all questions, behavioural anchors, and instructions'), distinguishing it from siblings like talent_assess_score or talent_assess_create_profile which handle different phases of the assessment process.

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 provides explicit guidance on when to use this tool ('Call this at the beginning of any onboarding conversation') and how it fits into the workflow with alternatives ('chatbot uses this to ask questions... then call talent_assess_score and talent_assess_create_profile'), clearly differentiating it from other assessment-related 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?

Annotations only provide a title, so the description carries the burden. It discloses the return values (automate, augment, coach, protect, hands_off) and that it recommends AI behavior, which is useful. However, it doesn't mention error handling, performance characteristics, or whether it requires an existing profile, leaving behavioral gaps.

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

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

The description is a single, efficient sentence that front-loads the purpose and includes key output information. There's no wasted text, making it highly concise and well-structured for quick understanding.

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 (2 parameters, no output schema, minimal annotations), the description is reasonably complete. It covers the purpose and outputs, but lacks details on error cases or integration with sibling tools (e.g., dependency on a profile from talent_get_profile). It's adequate but could be more 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 the schema fully documents the two parameters (name and task_description). The description adds no additional parameter details beyond what's in the schema, such as format examples or constraints, resulting in a 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 clearly states the verb ('classify') and resource ('task'), specifying it's according to the user's Talent-Augmenting Layer profile. It distinguishes from siblings by focusing on task classification rather than profile management or assessment, though it doesn't explicitly contrast with similar tools like talent_assess_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 implies usage when a task needs classification based on a user's profile, but provides no explicit guidance on when to use this versus alternatives (e.g., talent_assess_score for scoring tasks) or prerequisites (e.g., whether a profile must exist first). It's contextually appropriate but lacks detailed direction.

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?

The description adds valuable behavioral context beyond annotations by specifying the output format ('compact JSON block'), its intended use ('injecting into any LLM system prompt'), and content details ('friction levels, coaching domains, red lines, and interaction preferences'), though it lacks information on permissions, rate limits, or error handling.

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 and efficient, consisting of two sentences that directly convey purpose and output without unnecessary words, making it easy for an AI agent to parse and apply.

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 (single parameter, no output schema), the description provides sufficient context by explaining the output format and content, though it could benefit from more details on behavioral traits like error cases or usage constraints to be fully comprehensive.

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

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

With 100% schema description coverage, the input schema already documents the single required parameter 'name' as 'User's name.' The description does not add further parameter details, so it meets the baseline of 3 without compensating for any gaps.

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') and resources ('Talent-Augmenting Layer calibration settings for a user'), and distinguishes it from siblings like talent_get_profile or talent_get_progression by focusing on calibration settings rather than profiles or progression data.

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

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

The description implies usage context by mentioning the output is 'suitable for injecting into any LLM system prompt,' but does not explicitly state when to use this tool versus alternatives like talent_get_profile or talent_suggest_domains, nor does it provide 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?

The description adds behavioral context beyond annotations: it specifies that this returns a 'full profile' with detailed components, which helps the agent understand the scope of data retrieved. However, it doesn't mention potential errors (e.g., if the profile doesn't exist) or performance aspects, leaving some behavioral traits uncovered.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by usage guidance in the second. Both sentences earn their place by providing essential information without redundancy, making it highly concise 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 (retrieving a full profile with multiple components), the description is mostly complete: it explains what the tool does, when to use it, and what it returns. However, without an output schema, it doesn't detail the exact structure of the returned profile, leaving some ambiguity about the response 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?

The input schema has 100% description coverage, with the 'name' parameter documented as 'User's name (e.g., 'Angelo')'. The description adds no additional parameter semantics beyond this, so it meets the baseline of 3 where 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 specific action ('Load a Talent-Augmenting Layer profile') and resource ('by name'), distinguishing it from siblings like talent_list_profiles (list) or talent_save_profile (save). It specifies the return content ('full profile with expertise map, calibration settings, task classification, and red lines'), making the purpose explicit and differentiated.

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

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

The description provides explicit usage guidance: 'Use this at the start of every conversation.' This clearly indicates when to use it versus alternatives like talent_get_calibration or talent_get_progression, which might be for specific profile aspects rather than the full profile.

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?

Annotations provide a title ('List Profiles') but no other hints (e.g., readOnlyHint, destructiveHint). The description adds minimal behavioral context beyond the title, stating it lists 'all available' profiles, which implies a read-only, non-destructive operation. However, it lacks details on pagination, ordering, or what 'available' means (e.g., active vs. archived), leaving gaps in behavioral understanding.

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 directly states the tool's function without unnecessary words. It is front-loaded and efficiently communicates the core purpose, making it easy 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's simplicity (0 parameters, no output schema, minimal annotations), the description is adequate but incomplete. It covers the basic purpose but lacks context on usage versus siblings, behavioral details like response format or limitations, and doesn't leverage the absence of an output schema to explain return values. For a list operation, more guidance on what 'all available' entails would improve 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?

The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to explain parameters, and it appropriately doesn't mention any. A baseline of 4 is given since no parameters exist, and the description doesn't introduce confusion about inputs.

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 resource ('Talent-Augmenting Layer profiles'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'talent_get_profile' (which presumably retrieves a specific profile) or 'talent_save_profile' (which creates/updates profiles), missing the opportunity to clarify this is a bulk retrieval operation versus individual profile 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?

No guidance is provided on when to use this tool versus alternatives. Given sibling tools like 'talent_get_profile' (for single profiles) and 'talent_save_profile' (for creating/updating), the description should indicate this is for retrieving all available profiles in bulk, but it offers no such context or exclusions.

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

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

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.