eyepup

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

With no annotations provided, the description carries the full burden and does disclose that the tool returns an 'LLM-grounded answer with evidence and ranked actions.' However, it omits relevant behavioral caveats such as possible nondeterminism, latency, or whether the answer reflects live or cached analytics data, leaving clear 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 two sentences with the main purpose front-loaded, followed by concrete examples and a brief statement of what is returned. Every sentence earns its place and there is no redundant text.

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, the schema fully documents inputs, sibling tools provide context, and the description explains the output type at a useful level. A minor gap is not mentioning default lookback or limitations, but the absence of an output schema is reasonably compensated by the 'evidence and ranked actions' phrase.

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% and each parameter (question, days, site) already has a clear description. The tool description adds no extra semantic detail beyond the schema, so the baseline score of 3 applies.

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

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

The description uses a specific verb+resource pairing ('Ask... about your site visitors') and gives concrete example queries, making it unmistakable what the tool does. It also distinguishes itself from structured siblings like eyepup_log and eyepup_visitors_hot by emphasizing free-text natural-language questions.

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

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

The description explicitly says 'Use this when...' and provides example user phrasings, giving clear context for when to invoke it. It doesn't explicitly list exclusions or alternative tools, but the free-text scope strongly implies it is the natural-language counterpart to the more structured sibling 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 discloses that the dossier agent will reason about visitors profiled after the log row, affecting friction-pattern recovery grading. This is a meaningful side effect beyond the mere act of logging, and with no annotations, it carries the transparency burden well. It doesn't cover all potential behaviors but is sufficient for a logging action.

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

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

It's exactly two sentences, with the main action front-loaded and the contextual explanation in the second sentence. 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?

The description covers the core purpose, when to invoke, and a key behavioral effect, which is strong given no annotations or output schema. However, it omits guidance on parameter values (e.g., what 'kind' options mean, how to specify sites/paths), so it's not fully complete for a 5-param 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 adds no details about the five input parameters. With only 40% schema coverage (paths and title have descriptions), the remaining parameters (kind, site, description) are undocumented in both schema and description, leaving the agent to infer their meanings. The description does not compensate for this gap.

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

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

The description opens with the imperative 'Log a change you just shipped,' clearly identifying the action and object. The title reinforces this. It distinguishes from siblings by focusing on logging after a UX edit, unlike ask/todo/visitor tools.

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

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

It explicitly states 'CALL THIS after every UX edit,' providing a concrete trigger condition. It doesn't mention alternatives or when not to use it, but the context of siblings makes this clear enough; the instruction is unambiguous about the intended use case.

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

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

No annotations are provided, so the description carries the burden. The verb 'Get' and the phrase 'Each row carries a paste-ready recommended_action' communicate the read-only, output-oriented behavior. It doesn't disclose side effects, but none are expected for a list operation. The description adds context about ranking by impact score, which is beyond the schema.

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

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

Three sentences: what it does, when to use it, and what output to expect. Front-loaded with the primary verb. No fluff.

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

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

The description explains the output format (rows with paste-ready recommended_action), the ranking criterion (impact score), and the intended use case. With no output schema and no annotations, this is sufficient for a low-complexity read-only tool. It could have explicitly stated its read-only nature, but 'get' and 'list' imply non-mutating behavior.

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 descriptions already cover both parameters ('Apex-domain scope' for site, 'Max patterns (default 5)' for limit) at 100% coverage. The tool description does not add parameter-specific semantics, but given the schema's completeness, this is acceptable per the baseline.

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

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

The description opens with 'Get the top friction patterns ranked by impact score,' which is a specific verb+resource with a clear scope (top-ranked pattern list). The title 'List the friction-pattern To-Do queue' further reinforces this. It is clearly distinct from siblings like eyepup_ask and eyepup_log, so the purpose is 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 instructs to 'Use BEFORE making any UX edit' and explains the rationale (highest-impact pattern is more valuable than the user's immediate request). This provides a clear contextual trigger. It doesn't list alternative tools to use instead, but the guidance is explicit enough for this simple tool.

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

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

With no annotations, the description carries the full burden. 'Fetch' clearly indicates a read-only operation with no side effects. It describes the nature of the return value ('full LLM-written profile'), though it does not mention error cases or permissions. This is transparent enough for a simple retrieval tool.

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

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

The description is two sentences with no redundant words. The core action is front-loaded, and the usage guidance is succinctly appended. Every word earns its place.

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

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

For a one-parameter lookup tool, the description covers the why, what, and when. The lack of an output schema is mitigated by the description saying it returns the 'full LLM-written profile'. It does not detail error handling, but that is not essential for this simple tool.

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

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

The input schema already fully describes distinct_id as a 'UUID-shaped distinct_id' with 100% coverage. The description only repeats 'by distinct_id' without adding meaning beyond the schema, so it hits the baseline for schema-heavy tools.

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 ('Fetch') and resource ('full LLM-written profile for a single visitor'), with the required parameter ('by distinct_id'). It distinguishes itself from sibling tools by focusing on a specific visitor's detailed profile, contrasting with list or task-oriented tools.

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

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

The description explicitly provides usage context: 'Use after eyepup_visitors_hot or eyepup_todo to dig into a specific visitor.' This tells the agent when to invoke this tool and names the preceding tools, making the workflow explicit.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says it returns 'active high-intent visitors' but does not clarify how 'currently active' is defined, whether a site parameter is required, how the limit parameter affects results, or what the return format looks like. This is minimal 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: one sentence for purpose plus a short usage clause. Every word earns its place, with no fluff or repetition. It is well-front-loaded and easy to scan.

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 no output schema and no annotations, the description is incomplete. It tells the agent when to use the tool but not what the returned data looks like or how parameters behave. The agent can infer intent but not confidently compose correct invocations.

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

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

Schema description coverage is 0%, and the description does not mention either 'site' or 'limit' at all. It does not explain that 'site' likely filters by domain or that 'limit' caps the number of results, leaving the agent with no semantic guidance beyond raw schema names.

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 function with a specific verb and resource: 'Return the top high-intent visitors currently active.' It distinguishes itself from the sibling 'eyepup_visitor' by focusing on the 'top' and 'high-intent' aspect, so the agent knows this is for ranked visitor recommendations.

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 examples: 'Use when the user asks 'who's hot', 'who's about to convert', 'who should I focus on'.' This gives clear context for when to invoke the tool. However, it does not state when not to use it or mention alternatives among siblings, so it just misses a perfect score.

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