Measure.events Analytics

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

With no annotations provided, the description carries the full burden. It does disclose a key behavioral aspect—that the tool returns the site_key for tracking—but it omits details about side effects (e.g., whether existing sites can be duplicated), required permissions, or error behavior. For a simple create operation, this is adequate but not rich.

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

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

The description is a single, concise sentence that front-loads the action and the critical return value. Every word earns its place, with no waste or redundancy.

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

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

Given the simplicity of the tool (2 parameters, no output schema, no annotations), the description covers the essential information: what it does and what it returns. It does not discuss edge cases or constraints, but for this complexity level, it is reasonably 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?

The schema already provides 100% coverage with clear descriptions for both parameters ('Human-readable site name' and 'The site's domain'). The description adds no additional parameter-level meaning beyond what the schema gives, so the baseline of 3 applies.

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

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

The description clearly states the action ('Create a new site'), the target resource ('in Measure.events'), and the key output ('Returns the site_key needed for tracking'). It distinguishes itself from sibling tools, which are all read or tracking operations, making the purpose unmistakable.

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

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

While it does not explicitly name alternatives or exclusions, the description clearly implies when to use this tool (when a new site is needed) versus the siblings that retrieve data or log events. The context is clear enough for an agent to infer the appropriate usage without further guidance.

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

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

With no annotations provided, the description must disclose behavioral traits. It tells users what kind of insights to expect (spikes, drops, referrers) but does not mention return format, side effects, permissions, or latency. This is a minimal-but-adequate disclosure for a simple 'get' 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 a single, front-loaded sentence with a clear verb and object. It efficiently communicates the tool's purpose and provides illustrative examples without any 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 tool with no output schema and no annotations, the description offers enough context to understand the tool's function but leaves the response structure unspecified. It could be more complete by stating what the returned data looks like or listing any caveats, but given the low complexity, it is adequate.

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

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

The schema already provides a 100% description of the only parameter (site_key as 'The site's tracking ID'), meeting the high-coverage baseline. The description adds no additional parameter-level detail, but none is needed.

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

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

The description clearly states the tool retrieves proactive insights and anomaly detection for a site, with concrete examples (traffic spikes, drops, new referrers). This distinguishes it from siblings like get_site_analytics and get_site_summary, which presumably handle standard analytics and summaries.

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 detecting anomalies and proactive alerts, but it does not explicitly compare it to alternatives or state when not to use it. The word 'proactive' hints at the use case, but there is no exclusion or alternative referral, so guidance is implicit rather than explicit.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read-only operation ('Get') and lists the types of data returned, but it does not disclose details like default date ranges, pagination, or potential errors. This meets the minimum viable level but lacks richer 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 a single, front-loaded sentence that efficiently states the purpose and primary return data. It contains no wasted words, though it could be slightly more structured to include usage guidance or defaults.

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 absence of an output schema and annotations, the description is on the minimal side. It covers basic functionality and return types, but omits how it relates to sibling analytics tools and lacks details on the default period or range handling. For a simple read-only tool, this is adequate but not 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?

Schema description coverage is 100%, so the schema fully documents all parameters. The description only adds 'for a given period' which loosely references the period/to/from parameters but does not add meaningful extra meaning beyond what the schema already provides.

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

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

The description clearly identifies the tool's purpose with a specific verb ('Get') and resource ('analytics data for a Measure.events site'), and lists specific return types (pageviews, top pages, referrers). This is more detailed than a generic 'get analytics', but it does not explicitly differentiate from sibling tools like get_insights or get_site_summary.

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 the similar sibling tools get_insights and get_site_summary. The description does not mention any context, prerequisites, or exclusions, leaving the agent to infer usage purely from the name.

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 must carry the transparency burden. It clarifies that the output is a summary rather than raw data, but does not disclose potential limitations such as data freshness, permissions, or that results are a natural-language interpretation rather than exact figures. The read-only nature is implied by 'Get' but not stated explicitly.

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

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

The description is one concise sentence that front-loads the core action and key attributes (natural language, 7-day window, trends). Every phrase adds value, and there is no redundancy.

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

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

For a simple read-only summary tool with one parameter and no output schema, the description conveys the essential behavior and scope. It could optionally include return format details, but the core purpose is sufficiently clear without them.

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 sole parameter site_key is fully described in the schema (100% coverage), so the baseline is 3. The description adds no extra parameter-level context beyond what the schema already provides, but no additional clarification is necessary for a single self-explanatory 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?

The description clearly identifies a specific verb ('Get'), a specific resource ('natural language summary of site analytics'), and a defined scope ('last 7 days, including week-over-week trends'). This distinguishes it from sibling tools like get_site_analytics, which likely provides raw data rather than a synthesized narrative.

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 gives clear context about what the tool does but does not explicitly state when to use it over alternatives like get_insights or get_site_analytics. The phrase 'natural language summary' implies a use case, but there is no explicit when-not or alternative mention.

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. It implies a write operation (logging), but does not disclose potential side effects, prerequisites (e.g., site must exist), rate limits, or return behavior. For a mutation tool, this is a moderate gap.

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

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

Two sentences, front-loaded with the main purpose and followed by usage examples. Every word earns its place; 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 is adequate for a simple logging tool, but lacks mention of return values (no output schema) and prerequisites like needing a site created via 'create_site'. For a write operation, this is a clear gap.

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

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

Schema description coverage is 100%, so the parameters are already well documented. The description adds minimal extra meaning beyond examples of actions, which slightly overlaps with the schema's example 'published article'. Baseline 3 is appropriate.

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

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

The description clearly states the tool logs agent activity to a site's analytics, with examples like deployments and content updates. It uses a specific verb and resource, but does not explicitly differentiate from the sibling tool 'track_event', which may also log actions.

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

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

The description says 'Use this to record deployments, content updates, or any agent action on the site,' providing clear use cases. However, it does not mention when not to use it or alternatives, such as preferring 'track_event' for user events.

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 states what the tool does (tracks events) but fails to mention side effects (it writes/records data), whether the call is asynchronous, or what the response contains. This is a minimal description that leaves the agent guessing about outcomes.

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

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

The description is two sentences long, front-loaded with the core purpose, and contains no filler or redundant information. 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?

Given the complexity of 7 parameters, no output schema, and no annotations, the description is far too sparse. It does not describe the return value, error behavior, or how the parameters relate to the tracking action, leaving substantial gaps that an agent needs to handle 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 57%, leaving the three utm_* parameters completely undocumented in the schema, and the description does not compensate by explaining them. It also does not clarify how 'pageview or event' maps to the path or site_key parameters, adding little meaning beyond the schema.

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

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

The description clearly states the verb and resource: 'Track a pageview or event on a site.' It also specifies the use case ('instrument agent-driven page visits or actions') and is distinct from sibling analytics tools that read data (get_insights, get_site_analytics) and site creation (create_site).

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 says 'Use this to instrument agent-driven page visits or actions,' giving clear context for when to invoke. However, it does not provide explicit alternatives or when-not-to-use scenarios, and the sibling log_checkin may represent an overlapping use case.

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