Incisory

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

No annotations are provided, and the description does not disclose behavioral traits such as whether the tool is read-only, requires authentication, or has side effects. 'Show' implies read-only, but this is not explicit.

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

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

The description is a single concise sentence that front-loads key outputs. However, it could be structured with bullet points for clarity, but no excess words are present.

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 mentions outputs (visits, value, snippet) but fails to explain parameters, output format, or any constraints. Given the lack of output schema and annotation, more detail is needed for 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?

With 0% schema description coverage and no parameter explanations in the description, the meaning of 'days' and 'token' is entirely unclear. The description adds no value beyond the bare parameter 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 that the tool shows AI assistant visits and value over 30 days and provides a tracking snippet. Verb + resource are specific, but it does not explicitly distinguish from sibling tools like 'audit_visibility' or 'brand_accuracy'.

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

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

No guidance on when to use this tool versus alternatives, nor any prerequisites or limitations. The description only states what the tool does without contextual usage hints.

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 provided, so description fully covers behavioral traits: reads website, queries three AI models, returns specific outputs, and takes ~60s. No contradictions with annotations.

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 to three dense sentences, front-loaded with purpose, then details. No wasted words; every sentence contributes meaningful information.

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?

Description covers purpose, prerequisites, behavior, outputs, and timing. Without an output schema, it adequately describes return values. Slightly lacking on precise output format (e.g., data structure).

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

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

Schema coverage is 100%, but the description adds context (e.g., 'Pass just the website' implies minimal input, optional parameters are explained in context). Adds value beyond schema definitions.

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

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

Clearly states it runs an AI-visibility audit for a business, specifying the action, target, and outputs. Distinguishes from siblings like 'my_visibility' and 'visibility_history' by focusing on the audit 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?

Provides prerequisites (Incisory account, free/re-run policy) but does not explicitly guide when to use this tool versus alternatives like 'my_visibility' or 'visibility_history'. Context is implicit, not directive.

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 full burden. It discloses weekly runs, paid plan requirement, and output categories (correct, outdated, likely-wrong, missing, and fix). It does not mention if it is read-only or destructive, but the context implies a safe scan.

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, front-loaded with key purpose, and no extraneous information. Every sentence contributes value.

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, no annotations, and one undocumented parameter, the description explains output and frequency but lacks parameter guidance and behavioral details like rate limits or auth needs.

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 only parameter 'token' has 0% schema description coverage, and the tool description does not explain its purpose or usage. Minimal baseline compensation is absent.

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 performs a brand-accuracy scan, checking AI claims against the real web and providing a fix. It uses specific verbs and resource, and distinguishes itself from sibling tools by focusing on brand accuracy.

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 mentions it runs weekly on the paid plan, giving some context on frequency and plan requirement, but does not explicitly state when to use this tool versus alternatives or provide 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?

The description notes that the tool lists automated work 'over the last N days', but does not disclose any behavioral traits such as destructive potential, authentication requirements, rate limits, or what happens if the 'days' parameter is omitted. Since no annotations are provided, the description carries full burden and is insufficient.

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 that is front-loaded with the action and examples. Every word is functional, with no padding 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 lack of output schema and annotations, the description is too sparse. It does not explain the return format, default behavior for 'days', authentication requirements, or error scenarios, making it incomplete for an agent to use confidently.

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%. The description only hints at the 'days' parameter ('over the last N days') but does not explain its format or range. The 'token' parameter is completely unmentioned, leaving its purpose unclear. This is inadequate for two 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 uses the verb 'List' and specifies the resource 'automated work' with concrete examples (scans, articles, Reddit threads). It clearly states the scope, but does not explicitly differentiate from sibling list tools like list_content_queue or list_reddit_queue, which could cause confusion.

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 about when to use this tool versus the many sibling list tools. The description implies it's for viewing automated work, but fails to say when changelog is preferred over more specific tools like list_earned_mentions.

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 full burden. It indicates a read-only operation and mentions authentication via token, but does not disclose error conditions, rate limits, or data freshness.

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: first defines the tool's function with specific details, second provides usage guidance. Every sentence is essential and 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?

The description explains the tool's purpose and usage modes adequately for a specialized tool, but lacks details on return format, pagination, and whether the data is real-time or from a cached scan.

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 0%, but the description adds meaning by grouping parameters into two modes (token vs. business/website/niche) and explaining their purpose. However, it does not clarify required combinations or formats.

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 shows third-party sources cited by AI where the user is not named, providing a prioritized list. This distinguishes it from siblings like 'audit_visibility' or 'brand_accuracy'.

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 explains two usage modes (account token or ad-hoc audit with business/website/niche) but does not explicitly guide when to use this tool over alternatives or exclude other 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?

No annotations exist; description compensates minimally by noting it runs weekly and requires a paid plan, but lacks details on side effects, permissions, or result format.

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 sentence long with a trailing phrase, focused and front-loaded, but slightly informal.

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 tool with one parameter and no output schema, the description covers purpose but misses parameter guidance, resulting in adequate but incomplete 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?

With 0% schema coverage, the description fails to explain the single 'token' parameter, leaving its purpose and format entirely unclear.

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 it provides a competitor decode explaining why AI cites a rival and offers actionable moves. It is specific and distinct from siblings like brand_accuracy or citation_gap.

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 mentions it runs weekly on the paid plan, implying a scheduled report, but does not explicitly guide when to use this tool versus alternatives or clarify 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?

With no annotations provided, the description carries the full burden. It discloses that the tool only drafts replies and never auto-posts, and that results are queued. However, it lacks details on authentication requirements beyond mentioning the token, and does not discuss rate limits or error handling. The description is adequate but not comprehensive.

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 wasted words. The first sentence front-loads the main purpose and key behavioral traits; the second sentence concisely explains the input modes. Every clause adds value.

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 moderate complexity (4 params, no output schema, no annotations), the description covers the tool's purpose, usage modes, and behavioral constraints. It lacks details about the return format, but the sibling tool list_reddit_queue suggests results are stored and retrievable. Overall, it provides sufficient context for an AI agent to decide to use this tool.

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

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

Schema description coverage is low (25%), so the description compensates by explaining the relationship between parameters: token vs (business, website, niche) as alternative inputs. It clarifies that niche, website, and business are ad-hoc search parameters, adding meaning beyond the schema. However, it does not specify formats or constraints.

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 action: search for high-intent Reddit threads and draft replies. It specifies the resource (Reddit threads) and the outcome (drafts queued for review). It distinguishes from sibling tools like list_reddit_queue which list previously found opportunities.

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 two usage modes: using an account token or passing business/website/niche directly. It clarifies that drafts are never auto-posted and are queued for human review. It does not explicitly state when not to use the tool, but the context with sibling tools implies that list_reddit_queue is for viewing results.

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 full burden. It implies a read-only fetch but does not disclose whether any side effects occur, required permissions, rate limits, or return format. The statement is too minimal for safe autonomous invocation.

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 serves its purpose without extraneous information. It is front-loaded and efficient.

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 only one parameter and no output schema, the description provides a minimal but functional overview. It lacks details on expected output format, error handling, or id validation, making it adequate but not complete for autonomous use.

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 0%, so description must compensate. It adds that the id is from a shareable result URL, which provides some meaning beyond the raw schema. However, it does not explain the format, constraints, or how to extract the id from the URL, leaving 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 it fetches an audit by its id from a shareable URL. The verb 'Fetch' and resource 'previously run audit' are specific. While sibling tools exist, this tool's focus on retrieving by id distinguishes it from most others, though not explicitly compared.

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?

There is no guidance on when to use this tool versus alternatives like 'my_report' or 'visibility_history'. The context that the id comes from a shareable URL is provided, but no exclusions or prerequisites are mentioned.

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 exist, so description carries full burden. It explains the use of token, return data, and connection link if not connected. Missing details like rate limits, permission scope, or side effects.

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 clear, front-loaded sentences with no unnecessary words. Every sentence adds value.

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, description lists returned metrics and handles the not-connected case. Missing time period or aggregation details, but overall sufficient.

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?

Only one parameter 'token' exists with 0% schema description coverage. Description merely says 'Uses your account token' without explaining what the token is or how to obtain it.

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 real Google Search Console and GA4 data with specific metrics (clicks, impressions, ranking, sessions). However, it does not differentiate from sibling tools like 'ai_traffic' which may also handle traffic 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 when connected Google account is needed and mentions behavior if not connected. No explicit when-not or alternatives provided.

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

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

The description discloses only that it lists drafts, a read operation. With no annotations, it fails to provide additional behavioral context such as ordering, pagination, rate limits, or any side effects. Basic verb alone is insufficient.

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 that efficiently conveys the core function and returned fields. No unnecessary words, though it could be slightly more informative without harming conciseness.

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

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

Given the simple resource (list drafts) and only one optional parameter, the description is mostly adequate but lacks parameter documentation and output format details (no output schema). It does not explain ordering or limits.

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 only parameter 'token' has no description in the schema (0% coverage) and the tool description does not explain its purpose or format. The description adds no meaning beyond the property name.

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

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

The description clearly states the verb (List), resource (generated article drafts), and key fields (title, target question, status). It distinguishes from sibling tools like list_earned_mentions or list_reddit_queue, which deal with different content types.

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

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

The description implies usage for personal drafts ('for your account'), but does not explicitly state when to use this tool vs alternatives like list_reddit_queue or list_earned_mentions. No guidance on prerequisites or limitations.

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?

It discloses that the tool never auto-sends, which is a key behavioral trait. However, with no annotations provided, more detail on authentication or rate limits would be beneficial.

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, just two sentences, and front-loaded with the key purpose and constraint.

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 does not mention return format, pagination, or authentication requirements, leaving significant gaps for a tool with no output schema.

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 'token' has 0% schema description coverage, and the description provides no explanation of its purpose or usage.

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 it lists pending outreach pitches that are pre-drafted for the user to send, distinguishing it from siblings like list_content_queue and list_reddit_queue.

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

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

The description implies usage for viewing pending pitches that are not auto-sent, but does not explicitly state when to use versus alternatives or provide any 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?

Without annotations, the description should disclose behavioral traits. It only states the listing scope and verdicts but does not explain side effects, authentication needs, rate limits, or response structure. The tool appears read-only but this is not explicitly stated.

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, efficiently conveying the tool's purpose without extraneous 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?

Given no output schema and an undocumented parameter, the description is insufficient for an agent to fully understand how to use the tool. The parameter 'token' is not explained, and the output format is unspecified.

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?

There is a single parameter 'token' with no schema description and the description does not mention it. With 0% schema coverage, the description fails to clarify the parameter's purpose or usage.

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 lists tracked buyer questions (prompts) that are re-asked weekly, including each AI engine's latest verdict. It distinguishes from siblings like track_prompt by specifying a listing operation.

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 such as track_prompt or other list tools. Prerequisites or context of use are not mentioned.

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 must fully disclose behavior. It only states it lists pending opportunities, but doesn't mention if it is read-only, if it requires authentication, or what happens with empty results. No side effects or constraints are noted.

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 12-word sentence, which is very concise. It front-loads the main action. While structure could be improved with bullet points, it's not necessary for such a simple 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?

Given the tool's simplicity (1 optional param, no output schema), the description covers the basic functionality. However, it lacks description of the return value or format, which would be expected without an output schema.

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 mention the 'token' parameter at all, despite schema coverage being 0%. The phrase 'for your account' vaguely implies authentication, but no explicit guidance on the parameter's purpose or usage.

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 lists pending Reddit opportunities, specifying they include copy-paste drafts and thread links for the user's account. It differentiates from sibling 'find_reddit_opportunities' which likely searches for new opportunities.

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

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

No guidance on when to use this tool versus alternatives like 'find_reddit_opportunities' or prerequisites such as authentication. The description omits context for proper selection.

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 full burden. It vaguely mentions 'leaves the queue' but does not disclose side effects like required permissions, reversibility, or that it updates the status field.

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, which is concise but lacks sufficient detail to be effective. It is not poorly structured but could benefit from being more informative.

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 annotations, no output schema, and 0% parameter coverage, the description is incomplete. It does not explain prerequisites, return values, or how the tool fits into the workflow beyond the basic action.

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 0%, and the description adds no meaning to the parameters (id, token, status, published_url). It does not explain that status must be 'published' or 'dismissed' or when published_url 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 states the tool marks a draft as published or dismissed to remove it from the queue, clarifying the verb and resource. It distinguishes from sibling tools like 'mark_earned_mention' by focusing on drafts and queue management.

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 after publishing or deciding to skip, but does not explicitly state when not to use or mention alternatives such as 'write_article' or 'list_content_queue'.

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, and the description only discloses that marking will stop the pitch from appearing in the queue. It does not explain whether the action is irreversible, if it requires specific permissions, or what happens to the pitch data (e.g., deletion vs status change). This leaves behavioral traits 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 short sentence that is front-loaded and to the point. It efficiently conveys the main use case without unnecessary detail, though it could be expanded to cover parameters.

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 3 parameters, no output schema, and no annotations, the description is too minimal. It does not explain return values, error behavior, or prerequisites (e.g., needing the id from list_earned_mentions). This leaves significant gaps for an agent to use 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?

The input schema has 3 parameters with only 33% coverage (only 'id' has a description). The description does not mention any parameters, nor does it explain how to use 'status' or 'token'. Since schema coverage is low, the description should compensate but fails to provide any parameter guidance.

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 title explicitly states the tool marks a pitch as 'sent or dismissed', and the description clarifies it is used to stop the pitch from appearing in the queue. This clearly conveys the purpose and distinguishes it from sibling tools like 'list_earned_mentions' that list pitches.

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 a scenario ('after you send a drafted pitch yourself or decide to skip it') but does not explicitly state when not to use this tool, nor does it mention alternatives. For example, it does not clarify if you can reverse the action or if there are other ways to manage the queue.

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 provided, so description carries full burden. It explains that marking removes from queue, but doesn't mention idempotency, auth needs, or return 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?

Single sentence, no fluff, front-loaded with when to use. Every word adds value.

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 3 parameters, no output schema, and no annotations, description is minimally adequate. Lacks details on return value, error states, or parameter validation.

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 only 33% (only 'id' documented). Description does not explain parameters like 'token' or 'status' beyond their schema types.

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?

Clear verb 'mark' and resource 'Reddit opportunity' with specific outcome: 'stops appearing in your queue'. Distinguishable from siblings like 'list_reddit_queue' which is read-only.

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

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

Explicitly states when to use: 'After you post a drafted reply yourself (or decide to skip it)'. No alternative tools mentioned but context is clear.

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 full burden but only states it returns a report. It does not disclose whether the operation is read-only, authentication requirements beyond token, rate limits, or any side effects.

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 action and resource. No wasteful words; parentheses add specific detail without 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 absence of an output schema and the tool's specific content (verbatim answers, quick wins), the description lacks sufficient detail about the report format, how to interpret results, or what constitutes a 'subscribed client'. It is too vague for an agent to use confidently.

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 0% with no description for 'token'. The description adds context by stating 'using your account token', which clarifies its purpose. However, it does not explain token format or acquisition.

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 that the tool retrieves a 'full latest weekly report' for a subscribed client, with specific content types ('verbatim answers, quick wins'). It uses a verb implicitly (get) and identifies the resource uniquely among sibling tools like 'get_audit' or 'my_visibility'.

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 explicit guidance on when to use this tool versus alternative report/audit tools. The description only mentions using an account token but does not specify prerequisites, exclusions, or comparative scenarios.

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 lacks disclosure of behavioral traits: whether the operation is read-only, authentication details beyond 'using your account token', rate limits, or error scenarios. The description hints at a snapshot but doesn't confirm non-mutating 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?

Single sentence, no fluff. Purpose and target audience are front-loaded. Highly efficient.

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?

Despite no output schema, the description does not detail the return format, fields, or interpretation of 'AI-visibility snapshot + trend'. The optionality of token is implied but not clarified. Adequate for a trivial tool but incomplete for an agent to fully understand output.

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

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

Schema coverage is 100% (one parameter). The parameter description in the schema explains where to get the token and OAuth bypass, which adds value. The tool description minimally reiterates 'using your account token', adding no new semantic depth. 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 provides the latest AI-visibility snapshot and trend for a subscribed Incisory client. It distinguishes from siblings like 'visibility_history' by specifying 'latest', but doesn't explicitly differentiate from 'my_report' or 'ai_traffic'.

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

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

No guidance on when to use this tool versus alternatives like 'visibility_history' or 'my_report'. Does not mention prerequisites, exclusions, or context for use.

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 full burden. It explains the behavior of setting autonomy to one of three modes but does not disclose side effects (e.g., whether this overrides previous settings) or required permissions. A 3 is adequate for a simple setter.

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 no wasted words. Every part contributes to understanding the tool's purpose and parameters.

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 setter with one enum parameter and no output schema, the description covers the essential aspects: what it does, the available options, and their meanings. It is complete enough for an agent to invoke 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?

The schema has 0% description coverage, so the description must compensate. It adds meaning for the 'autonomy' parameter by explaining each enum value. The 'token' parameter is not described, but it is likely a common authentication field. Thus, the description adds significant value beyond the schema.

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

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

The description uses a specific verb ('Choose') and clearly identifies the resource ('how much Incisory does on its own each week'). It distinguishes from sibling tools by focusing on autonomy settings, which no other sibling addresses.

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 states the context ('each week') and explicitly lists the three possible values with plain-English explanations. While it doesn't provide when-not-to-use guidance, the tool is simple and has no obvious alternatives among siblings.

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?

Discloses that tracked questions are re-asked weekly across ChatGPT/Gemini/Perplexity and that the max is 12. Since no annotations exist, this description provides necessary behavioral context for the correct 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?

Two sentences: first introduces the action with an example, second explains the outcome and limitation. Every sentence adds value, and the verb 'Add' is front-loaded for immediate clarity.

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

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

For a tool with 2 parameters and no output schema, the description covers the action, purpose, example, operational mechanism (weekly re-asking), and a constraint (max 12). It is sufficiently complete for a 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?

Schema documentation covers 50% of parameters (text described, token not). The description adds an example of a buyer question and explains the question's role, partially compensating for the undocumented token parameter. Could be improved by explaining token's purpose.

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 the verb 'Add' and specifies the resource 'a question your buyers ask' to a tracked set. It distinguishes the tool's function from siblings like list_prompts (which lists prompts) by focusing on adding a new item.

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?

Provides clear context: tracks buyer questions to see a per-question trend across AI models, and states the max limit of 12 tracked questions. Does not explicitly mention when not to use or suggest alternatives, but the purpose is well-defined.

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 fully disclose behavior. It mentions the checkout link and trial, but does not state side effects like idempotency, authentication requirements, or behavior if already subscribed.

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 no unnecessary words. Every part is relevant and informative.

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 no parameters and no output schema, the description adequately covers what the tool does and the plan details. It is complete for its simplicity.

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?

There are no parameters, and schema coverage is 100%. The description adds value by explaining the plan details and the purpose beyond the empty 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 action ('Get the Stripe checkout link to subscribe') and the specific resource (one plan, €49/mo, includes free trial). It distinguishes from sibling tools by focusing on billing/subscription.

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 context for using the tool is clear: to subscribe to the plan. However, it does not explicitly mention when not to use it or provide alternatives for related tasks like viewing billing history.

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 must fully disclose behavior. It states it reads your own history, implying read-only, and mentions OAuth/token usage. However, it does not describe the output format, idempotency, or error cases (e.g., no data). Additional behavioral details are missing.

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 redundancy. The first sentence states purpose, the second adds authentication context. It is front-loaded and every sentence is necessary.

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, the description should hint at the return structure. It mentions 'trend across weekly snapshots' but does not specify format (e.g., array of objects with date/score). For a simple tool, it is partially complete but could be improved.

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

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

Schema coverage is 100% for the single parameter 'token', and its description is clear. The tool description adds context about authentication binding but does not significantly enhance meaning beyond the schema. Per guidelines, 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 retrieves a visibility trend across weekly snapshots, which indicates the resource and action. It distinguishes from siblings like 'my_visibility' (likely current snapshot) by explicitly mentioning 'history'. However, it lacks a strong verb like 'retrieve' or 'view'.

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 mentions authentication and that only own history is accessible, but it does not specify when to use this tool versus alternatives (e.g., 'my_visibility' or 'audit_visibility'). No explicit when-to-use or when-not-to-use guidance is provided.

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

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

No annotations provided, so the description carries the full burden. It discloses that output is a draft stored in a queue and not published, but omits potential behaviors like error handling, rate limits, or overwrite policies. Adequate but not exhaustive.

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-load the purpose and output, followed by usage alternatives. No 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 five parameters, no output schema, and no annotations, the description covers core functionality but leaves gaps: parameter dependencies (e.g., can token and business be combined?), validation rules, and error cases. The output description is helpful but incomplete for a full understanding.

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 low (20%), and the description adds meaning to token, business, website, and niche as alternative authentication/context inputs. However, it lacks detail on parameter formats, constraints, or interaction behavior. Does not fully compensate for the schema 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 generates a branded article to be cited by AI assistants for a specific buyer question, distinguishing it from sibling tools like audit_visibility or citation_gap. It specifies the output components (title, body, FAQ schema) and storage location (content queue).

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 explains the tool returns a draft for review, not auto-published, and offers two usage modes (account token vs. ad-hoc via business/website/niche). It does not explicitly contrast with sibling tools, but the context implies its unique role for article generation.

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