DevDome Analytics

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

With no annotations provided, the description carries the full disclosure burden, and it handles this well: it discloses the mutation side effect (adds a site), the return value (tracking snippet to install), and the resulting state transition (pending until verified). It does not cover failure cases, idempotency, or re-adding an existing domain, so it is not a 5.

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 zero waste: the action is front-loaded, and the second sentence packs both the return value and the state behavior into one efficient clause. No filler or redundant restatement of the tool name.

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

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

For a single-parameter creation tool with no output schema, the description is nearly complete: it covers the action, the return value (tracking snippet), and the post-call state (pending). The only meaningful gap is not explicitly telling the agent to use verify_site as the follow-up step, though the pending-until-verified wording strongly hints at it.

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

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

Schema description coverage is 100%, so the baseline of 3 applies. The single domain parameter is already fully documented with a concrete example (example.com) in the schema; the description adds no parameter-specific meaning, but at full coverage it does not need to.

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?

"Add a website to the account" states a specific verb and resource with no ambiguity. It naturally distinguishes itself from all siblings, which are read/analysis operations (get_*, list_*) or the later verification step (verify_site). An agent can tell this is the creation mutation within the tool family.

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

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

The phrase "the site stays pending until verified" implies the add-then-verify workflow and hints that a separate verification step is needed, which maps to the sibling verify_site. However, it never explicitly names the alternative or states when not to use this tool, so the guidance remains implied rather than direct.

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 full burden of explaining behavior. It adds value by defining 'bots' as known-bot catalog activity and 'threats' as suspicious/behavioral detections, but it never discloses what the returned report contains, default behavior when kind/days are omitted, or time-window 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?

Two compact sentences with no filler; the first scopes the subject to a site's bot traffic and the second unpacks the otherwise-cryptic kind parameter. The dash/semicolon compression is slightly dense, but the description stays well within an appropriate size.

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

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

There is no output schema and no annotation coverage, so the description alone must let an agent predict the call's result. It never states the response shape (counts, percentages, time series), the effect of omitting kind or days, or the meaning of the days window, which is a real gap for a report 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?

With 0% schema description coverage, the description must compensate, and it does for 'kind' by explaining the meaning of both enum values. It adds nothing for 'days' (units/range/default) or 'site' beyond the obvious, leaving two of three parameters under-documented.

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 lead phrase 'Bot traffic on a site' names the resource and domain, and the kind definitions clarify what the report distinguishes. The retrieval verb is only implied by the tool name rather than stated in the description, and it does not contrast with the sibling get_breakdown, so it misses full sibling differentiation.

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 querying bot-traffic data for a given site and gives meaning to the two kind filters, which helps an agent decide whether bots or threats are wanted. However, it never states when to prefer this over get_breakdown or provides any exclusion criteria, so usage guidance is implied rather than explicit.

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

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

No annotations are provided, so the description must disclose behavior itself, but it only states 'Top-N breakdown' and 'by one dimension'. It does not explain result shape, default limit, sorting, time-range handling, or edge cases.

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 short, front-loaded sentence with no redundant wording. It is concise and scannable, though it achieves conciseness by omitting meaningful detail.

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

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

With no output schema and no annotations, the description is too thin to fully support correct invocation. It does not explain the return payload, time-range parameters, limit behavior, or dimension-specific results, leaving too much to inference.

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%, yet the description only clarifies site, dimension, and the Top-N concept. It leaves to, from, days, and limit semantics to their parameter names without describing formats, defaults, or interactions.

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 identifies a top-N traffic breakdown grouped by one dimension, naming both the operation and the resource. It is clear on its own but does not explicitly differentiate it from sibling analytics tools like get_stats or get_timeseries.

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 given on when to use get_breakdown versus the sibling tools. The word 'breakdown' implies a use case, but there are no conditions, exclusions, or alternative routing cues.

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 full burden. It does disclose the 5-minute window and the returned fields (current page, path), but it does not explicitly say the operation is read-only, nor mention edge cases such as empty results, pagination, or rate limits.

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

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

The description is a single concise sentence with no filler. The key constraint (last 5 minutes) is front-loaded, and every word contributes to understanding the tool.

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

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

For a simple one-parameter read tool, the description covers the core concept, but it lacks output structure details and clear parameter value guidance. The presence of similar sibling tools like get_visitors also makes the missing differentiation more noticeable.

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 0% description coverage and only defines 'site' as a string. The description merely says 'on the site,' which gives some context but fails to clarify whether the value should be a site ID, domain name, or slug, leaving the parameter semantics under-specified.

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 what the tool returns: visitors active in the last 5 minutes with their current page and path. It is specific and distinct from siblings like get_visitors or get_timeseries, though it lacks an explicit verb such as 'Retrieve'.

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

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

The phrase 'in the last 5 minutes' implies this is for real-time visitor data, which is a usage context. However, there is no explicit guidance on when to use this tool versus alternatives like get_visitors, and no exclusions or conditions are stated.

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

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

No annotations are present, so the description carries the transparency burden. It does state that the tool returns a summary with several health components, and the get prefix implies a read-only operation, but it does not mention data freshness, failure behavior, permissions, or whether checks are live or historical.

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 compact sentence with a front-loaded purpose and a scannable list of included health components. Every word adds useful information, with 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 one-parameter tool with no output schema, the description gives a reasonable high-level overview and return contents. However, it omits the required site identifier format and does not distinguish itself from verify_site, so an agent cannot fully invoke it correctly without additional inference.

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 only says 'for a site', which essentially restates the property name. The description does not clarify whether site should be a domain, URL, site ID, or other identifier, leaving the single required parameter ambiguous.

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 and resource: retrieving an uptime/health summary for a site. It lists concrete contents (status, response time, TLS/domain checks, open incidents) that separate it from the analytics-focused sibling 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 implies the use case: whenever a site's health summary is needed. However, it does not explicitly state when not to use the tool or which alternative to prefer, especially given verify_site may also perform site-related checks.

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 behavioral disclosure burden. It usefully notes that it defaults to the full retained history, but it does not describe the output shape, date-range interaction semantics, or the read-only nature beyond what the name implies.

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

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

The description is two short sentences with no filler. The primary purpose and distinguishing 'headline' concept are front-loaded, followed by a compact metric list and one key default behavior.

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 tool's purpose, metrics, and default behavior, which is sufficient for basic selection. But with no output schema and no annotations, it leaves out how from/to/days interact and what an agent should expect in the response beyond the KPI names.

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 75%, so the schema already documents most parameters. The description adds little beyond restating the full-history default, which the 'days' parameter already communicates. The metric list describes the output, not parameter semantics.

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 gives a clear verb+resource combination ('Headline KPIs for a site') and enumerates the exact metrics returned. The 'headline' qualifier helps distinguish it from specialized siblings like get_timeseries and get_breakdown, though it does not explicitly name them.

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

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

The description implies this is the go-to tool for high-level site metrics, and the KPI list signals when it would be relevant. However, it gives no explicit guidance about when to prefer a sibling tool or when not to use this one.

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 behavioral disclosure burden. It usefully reveals daily granularity, the included metrics, and that dates are in the site's timezone. However, it does not discuss output shape, date-range semantics, pagination, limits, or behavior when no data 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 a single sentence, front-loaded with the key phrase 'Daily traffic series', and contains no filler. It is compact and efficient, though it could trade a little brevity for parameter and output 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 zero schema description coverage, no annotations, and no output schema, this description is too thin. It tells the agent what the output contains but not how to construct a valid request or interpret the response, leaving important details to guesswork.

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%, so the description needed to clarify the parameters. It only hints at 'site' and 'daily' and mentions the site's timezone, leaving 'from', 'to', and 'days' semantics to be inferred from their names and the tool name. That is too weak for four undocumented parameters.

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 identifies the resource ('a site') and the deliverable ('daily traffic series') with a concrete metric list, making the core purpose clear. It does not explicitly contrast with sibling tools like get_breakdown or get_stats, but the time-series framing and metric list differentiate it well enough.

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

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

The description implies when to use this tool — when a daily traffic series for a site is needed — but provides no explicit when-not-to-use guidance, prerequisites, or pointers to alternatives. An agent can infer the basic use case, but nothing tells it to prefer a sibling for other analytical needs.

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 makes clear this is a read-only, per-visit listing with human/bot classification, which is useful. However, it does not disclose defaults (e.g., time window for 'recent'), result limits, ordering, or pagination behavior.

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

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

A single sentence with a colon-delimited field list, front-loaded with the core concept 'Recent individual visits'. Every word carries information and there is zero filler

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

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

For a tool with three parameters, no annotations, and no output schema, the description is too thin. An agent cannot tell the required site parameter, the default window, the meaning of limit, or how this differs from get_realtime and get_bot_report. The overall behavior and call contract are under-specified.

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%, so the description must compensate for the three parameters (site, days, limit). It only hints at semantics through 'Recent' (days) and 'individual visits' (limit), but never explains that site is required, what units days uses, or what limit caps. This is inadequate for an agent to set parameters confidently.

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

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

The description states a specific resource ('recent individual visits') and enumerates the data fields returned (entry page, path, country, device, human/bot). The phrase 'individual visits' implicitly distinguishes it from aggregate sibling tools like get_stats, get_breakdown, and get_timeseries, though it never names them explicitly.

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

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

Usage is implied: an agent can infer this tool is for retrieving recent per-visit details. However, there is no explicit guidance on when to prefer it over get_realtime or get_bot_report, no exclusions, and no mention of prerequisites like an existing site.

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?

There are no annotations, so the description carries the burden of disclosing behavior. It discloses the return concept (websites with verification status) and the scope (DevDome account), but it does not state whether the output is paginated, whether it includes all sites or only active ones, or whether the verification status is live-checked or cached. This is acceptable for a simple list operation but lacks deeper 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 short sentence with no filler. It front-loads the action ('List'), specifies the resource ('websites'), scopes it ('on this DevDome account'), and adds the key output field ('verification status'). 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 zero-parameter list tool with no output schema, the description is largely complete: the agent knows what resource is returned and what key attribute is included. The main missing piece is whether verification status is the only attribute and whether any filtering or sorting options exist, but with no parameters required, an agent can call this tool confidently without further 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 has zero parameters, so there are no parameter semantics to document. The description compensates by clarifying the implicit account scope and the output fields, making the tool's inputs effectively nonexistent. Baseline 4 is appropriate because there are no params to explain.

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 specific language: it lists websites on the DevDome account and includes verification status. It clearly identifies the resource and the operation, and it distinguishes itself from siblings like verify_site and add_site by focusing on listing rather than modifying. However, it does not explicitly contrast with sibling tools, so there is room for slight improvement.

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 a read-only listing context and mentions the account scope ('on this DevDome account'), which helps an agent know when to call it. It does not explicitly state when not to use it or point to alternatives like get_site_health for site details or verify_site for verification actions, but the context is reasonably clear for a simple zero-parameter 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 behavioral disclosure burden. It clearly discloses the two verification methods (installed snippet or DNS TXT record) and restricts operation to pending websites. It does not describe failure outcomes or state changes, but the verification behavior itself is accurately and helpfully exposed.

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 entire description is one front-loaded sentence with a useful parenthetical method detail. There is no fluff, redundancy, or preamble.

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 verification tool, the description covers what, when, and roughly how. Missing are the result/success semantics, what happens if verification fails, and whether an already-verified site can be re-checked. These gaps matter because there is no output schema to fall back on.

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 never mentions the 'domain' parameter or its expected format. The agent is left to infer that 'domain' means the site being verified, with no guidance about whether to pass a bare domain, a URL, or a domain matching a previously added site.

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 ('Verify') with a clear resource ('ownership of a pending website') and names the verification mechanism (snippet or DNS TXT record). This distinguishes it immediately from the sibling add_site and get_* 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 phrase 'pending website' gives clear context that this tool belongs to the site-verification workflow, coming after add_site and before tools that require confirmed sites. It does not explicitly name alternatives or exclusions, but the scope is evident.

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