painspotter

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

Annotations already cover readOnly, idempotent, and non-destructive safety. The description adds meaningful traits: 'Free; does not consume API quota' (cost/rate-limit behavior) and 'Content is an original AI synthesis with no verbatim community quotes' (content provenance). These go beyond 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?

Three short sentences, each adding a distinct fact (what is returned, cost, content nature). No fluff or repetition; the size is appropriate for the tool's simplicity.

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 tool is simple with one parameter, and the annotations plus output schema carry substantial structured info. The description covers the return format (Markdown body), cost, and content nature, making it complete for an agent.

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 fully documents 'slug' as coming from list_blog_posts. The description adds the constraint 'published', clarifying that only published posts are retrievable, which is extra semantic information 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 specifies the action ('Get'), the resource ('blog post'), and the exact scope ('full Markdown body by slug'), clearly distinguishing it from sibling tools like list_blog_posts. It is a specific verb+resource with no ambiguity.

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 is clear: use this when you have a specific slug and need the full Markdown body. The schema note about slug coming from list_blog_posts implies a follow-up workflow, but the description does not explicitly mention alternatives or when-not-to-use conditions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the list of returned data points, which is useful, but does not disclose any further behavioral traits (e.g., rate limits, error handling, pagination). This is acceptable given strong 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?

The description is a single, concise sentence with a clear verb-object structure and a useful enumeration of returned fields. The parenthetical '(Free tool)' is slightly unnecessary but does not detract from clarity or brevity.

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 single-ID fetch tool with strong annotations and an output schema present, the description covers the main action and return contents. It could mention behavior when the opportunity is not found, but that gap is minor given the tool's 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?

Schema coverage is 100%, so the parameter is already documented. The description adds provenance guidance ('taken from query_opportunities or get_overview results'), which tells the agent where to obtain the ID, exceeding the baseline for schema-covered 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 clearly states 'Get the full detail of a specific opportunity' and enumerates the specific data fields included (description, score breakdown, MVP features, etc.). This distinguishes it from sibling tools like query_opportunities (searching) and get_overview (high-level summary).

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

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

The parameter description says the opportunity_id is 'taken from query_opportunities or get_overview results', which implies the correct usage workflow (first find/overview, then get detail). It does not explicitly name alternatives or exclusion criteria, but the context is clear enough.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds behavioral context beyond annotations by noting it's a 'snapshot' of trending themes (implying point-in-time data) and a 'global overview' (implying full scope, no filtering). It also mentions 'Free tool,' which is a cost-related behavioral trait not covered by 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?

The description is two sentences plus a parenthetical tag, front-loaded with the primary purpose and then adding a usage suggestion and cost note. Every sentence earns its place with no redundancy or 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?

Despite having no parameters and a rich output schema, the description sufficiently explains what data is included, how it should be used as a first step, and clarifies its free cost. It is complete for a simple overview tool with comprehensive annotations.

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

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

The tool has zero parameters and the schema coverage is 100%, so there are no parameter semantics to explain. The description compensates by describing the richness of the output, which is more relevant than parameter details here. Baseline of 4 is appropriate for a no-parameter tool.

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 function with a specific verb ('Get a global overview') and resource ('PainSpotter'), listing concrete deliverables: all domain categories with theme count, opportunity count, 30-day mentions, and trending themes. It distinguishes itself from sibling tools by positioning itself as a broad overview before 'drilling in' with the others.

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

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

The description explicitly positions this tool as 'a good first step' and contrasts it with drilling in using other tools, providing clear when-to-use guidance. It doesn't name specific alternatives but the sibling context and general overview framing make the intended usage 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by listing the return contents and noting 'Pro tool', which warns about access restrictions. No contradiction 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?

The description is a single, information-dense sentence, front-loading the main purpose and then listing specifics. No wasted words; the 'Pro tool' note is useful.

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 an output schema and clear annotations, the description sufficiently covers the tool's purpose, key deliverables, and access level. It doesn't need to detail return values because the output schema exists.

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

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

The schema description covers the 'theme' parameter completely, explaining the accepted ID/slug format and source. The tool description adds no additional parameter semantics beyond 'single theme', so 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 'Get the full market signal for a single theme' and enumerates specific components (trend, mentions, channels, opportunities). This distinguishes it from sibling tools like get_overview and list_trending_themes, which serve different purposes.

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

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

The description implies usage when a single theme's detailed signal is needed. The parameter description references list_trending_themes / get_overview as sources, indicating the typical workflow. However, it doesn't explicitly contrast with alternatives or state when not to use it.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds non-obvious behavior: 'recent published' implies only published posts are returned, and 'does not consume API quota' is a unique operational detail not covered by annotations. This is valuable supplemental 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?

Two sentences, front-loaded with the core action and resource. The second sentence adds relevant context about content and cost without waste. Every phrase 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?

With only one optional parameter and an output schema available, the description covers the essential context: what the posts are about, that they are published and recent, and the free/quota behavior. This is complete for the tool's 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?

Schema coverage is 100% (limit parameter has a clear description). The tool description adds no additional parameter-specific meaning, so the baseline of 3 applies—schema does the heavy lifting.

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

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

The description uses a specific verb 'list' with resource 'recent published PainSpotter blog posts', clearly distinguishing it from siblings like get_blog_post (single post retrieval). It also adds context about the post content, making the purpose unmistakable.

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

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

The description clearly implies when to use this tool ('recent published blog posts') and adds a notable benefit ('Free; does not consume API quota'). However, it does not explicitly state when not to use it or mention alternatives beyond the sibling list, so it falls short of a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context: sorting by trend change %, aggregating multiple opportunities, and the data fields carried (30-day mentions, trend direction, audience clarity). It also notes 'Pro tool' access. 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 sentences, no fluff. The main action and purpose are front-loaded in the first sentence, and the second sentence concisely defines the domain concept. 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 simple list tool with 2 parameters, full schema coverage, and an output schema present, the description fully covers the tool's purpose, sorting behavior, data content, and use case. Nothing critical is missing.

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

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

Schema description coverage is 100%, with both limit and min_mentions fully documented in the schema. The description text adds no parameter-specific details beyond the schema, so it just meets the baseline without enhancing understanding.

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 ('List') and resource ('themes'), further scoped by 'currently heating up' and a concrete sort order ('trend change %'). It clearly distinguishes this tool from siblings like get_theme by describing the theme concept and trending focus.

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

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

The description explicitly states the intended use case ('to catch demand that is growing'), providing clear context for when this tool is appropriate. However, it does not explicitly mention when not to use it or name alternative tools (e.g., get_theme), so it stops short of full alternatives guidance.

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

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

Annotations already provide safety signals (readOnlyHint, openWorldHint, idempotentHint, destructiveHint: false). The description adds that results can be filtered and sorted, but these behaviors are already evident from the schema. No additional context like pagination, rate limits, or aggregation specifics is provided, though the term 'Unified' hints at combined sources without detail. No contradiction 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?

The description is a single, well-structured sentence that front-loads the core purpose ('Unified opportunity search') and then lists the key capabilities. The parenthetical '(Pro tool)' adds minor context without bloat. 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 tool with 7 optional parameters, rich schema descriptions, and an output schema, the high-level summary is sufficient to guide an agent. The description covers search, filtering, and sorting, while defaults and result structure are available in the schema. A small gap is the lack of explicit mention of pagination behavior, but the `page_size` parameter implies 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?

The input schema has 100% description coverage, so parameters are fully documented. The tool description merely repeats the parameter list (keyword, min_score, platform, etc.) without adding new semantic details. Per the rubric, baseline is 3 when schema coverage is high and the description does not compensate further.

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 specific verb 'search' with the resource 'opportunities' and explicitly lists the filter dimensions (keyword, minimum score, platform, recommendation tier, domain category) and sorting behavior. This clearly distinguishes it from sibling tools like get_opportunity (single item) or get_overview (category summaries). The purpose is completely unambiguous.

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

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

The description clearly establishes this as the 'unified opportunity search' tool, implying it is the primary tool for finding opportunities via multiple filters. However, it does not explicitly mention when not to use it or name alternatives like get_opportunity for direct lookup. The context is clear but exclusions are absent.

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