Shruti Kaushik: Founder GTM Research

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds the notable behavior that the payload includes a call-to-action (booking a call, free AI visibility check), which hints that the response may contain actionable links, but it does not clarify whether those are just text or executable.

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 front-loaded sentence with a dense parenthetical that earns its place by specifying niche and tenure. It is appropriately sized for a static informational tool, though the trailing triple-purpose clause ('what she offers, and how to book…') is slightly crammed.

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, the description carries the burden of describing returns; it does sketch the content categories (bio, offers, booking, AI visibility check) but says nothing about response format or whether the booking path is actionable, leaving a modest gap for an agent deciding whether this tool answers the user's request.

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 takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate, and it correctly does not invent any input options.

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 names the subject (Shruti Kaushik) and enumerates the content surfaced: her role as a LinkedIn/Reddit ghostwriter for B2B founders since 2015, her offers, and the booking/AI-visibility CTA. That is a clear informational purpose, though it never distinguishes itself from siblings like answer_question or get_guide.

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 only implied: an agent can infer this is the tool for 'who is Shruti / what does she offer' questions, but there is no explicit when-to-use, no exclusion, and no routing to answer_question or get_guide for adjacent 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 readOnlyHint=true and openWorldHint=false already declaring the safety profile, the description still adds real context: the answers are grounded in FAQ sections of guides/statistics pages, and each result carries its source URL. That grounding scope and the return shape are useful disclosures beyond the 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 tight sentences, front-loaded with the core action and corpus, followed by the return-content note. No filler or restatement of the title.

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 the description does cover the essential return shape (answers plus source URL). However, it leaves the 'limit' parameter entirely unexplained and gives no sense of answer volume or ranking behavior, which a retrieval tool of this kind should clarify.

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% for both parameters. The 'question' parameter is self-evident, but 'limit' (default 3, max 8) is undocumented in both schema and description, so the agent has no guidance on result-count control. The description compensates for neither.

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?

States a specific verb+resource: finding closest published answers to a B2B GTM question, and pins the corpus to FAQ sections of guides and statistics pages. It implicitly separates itself from search_guides/search_statistics by returning synthesized answers rather than raw pages, but it never names those siblings 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?

The description implies usage (use it when you have a GTM question you want a direct answer to) but gives no explicit when-to-use vs. search_guides or search_statistics, and no exclusions or prerequisites. An agent must infer the routing decision itself.

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 declare the safety profile (readOnlyHint=true, openWorldHint=false), so the description's job is to add return-value context, which it does: the score range, per-pattern flags, matched words, and reasons. It also notes it runs the same checker as a public tool, useful for trust. It stops short of stating determinism 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?

Two tight sentences with no filler; the core action and output shape come first, and the external-tool reference is a compact trailing clause. Every sentence earns its place.

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

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

For a one-parameter read-only checker with no output schema, the description supplies what the agent otherwise couldn't know: the numeric scale and the structure of the returned findings. Nothing needed to invoke or interpret the tool 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?

There is a single parameter with 100% schema coverage ('The full post text'), so the schema already carries the semantics. The description mentions a 'post draft', which mildly reinforces that the full text is expected, but adds no format or length details. 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?

States a specific verb (score) and resource (a LinkedIn post draft), plus the 0-100 scale and exactly what it detects: template patterns readers flag as AI-written. This is far more specific than a restated title, and no sibling tool overlaps with this scoring function.

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 makes clear this is for scoring a drafted LinkedIn post, which is an implied usage context, but it offers no explicit when-not guidance or named alternatives (the siblings are unrelated topic/guide tools). Adequate but leaves the agent to infer that the input must already be drafted text.

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 and openWorldHint=false, so the safety profile is covered, but the description adds no behavioral context of its own — no return format, no indication of whether this returns a page, structured data, or a summary. It only restates content scope.

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

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

It is a single sentence, but a comma-heavy one that front-loads a proper name rather than the tool's action, and the trailing content list is somewhat padded. It is not wasteful, but it is not optimally structured for agent scanning.

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 parameters, the description carries the burden of telling the agent what comes back. It lists content topics, which is partial compensation, but gives no sense of the response shape or depth for a knowledge-retrieval tool.

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

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

The tool takes zero parameters, so the schema requires no explanation and there is nothing for the description to disambiguate. Baseline 4 applies per the no-parameter rule.

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 being served (a page about Shruti Kaushik's founder-led AI visibility service) and enumerates its contents: the method, three tiers, monthly report, and baseline check. However, it never states a verb or what the tool actually returns — it reads as a content blurb rather than a tool action, and differentiation from the sibling about_shruti_kaushik is only implicit in the topic.

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 call this tool versus siblings like get_guide, list_topics, or about_shruti_kaushik, nor any exclusions or prerequisites. 'How to get a free baseline check' hints at a user goal but does not translate into a usage condition.

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 and openWorldHint=false, so the safety profile is covered. The description adds value beyond that by disclosing the return format (markdown) and the two retrieval modes. It omits error behavior for an invalid slug or unmatched heading, which keeps it out of 5 territory.

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 short sentences with no filler: the capability comes first, then the sequencing hint. Every clause earns its place, and the secondary routing advice is placed last where it does not delay the core purpose.

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 two-parameter read-only tool with no output schema, the definition covers purpose, mode selection, output format, and prerequisite. The only meaningful gap is failure behavior when the slug or heading does not resolve, which an agent would otherwise have to infer.

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 50%: 'section' is documented in the schema while 'slug' is bare. The description clarifies the section case ('one section of it by heading'), which usefully matches the heading-text semantics, but 'by slug' adds little beyond restating the parameter name. Reasonable compensation, not full.

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?

States a specific verb and resource ('Get the full text ... of one guide') and quantifies scope ('one guide'), including the two retrieval modes: whole document by slug or a single section by heading. The added '(markdown)' even pins the return format, so an agent knows exactly what it gets without opening the schema.

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?

'Use after search_guides' explicitly names the sibling that produces the slug this tool consumes, giving a clear ordering rule. It stops short of stating when not to use it or what to do if the slug is unknown, so it lands at 4 rather than 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=true and openWorldHint=false, so the safety profile is covered. The description adds that only *published* items are returned and that URLs are included, but says nothing about ordering, pagination, or result size.

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 front-loaded sentence with no filler; the scope ('every published guide and every statistics topic page') and the returned artifact ('with URLs') are stated immediately.

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, no-output-schema listing tool, the description conveys the catalogue scope and return contents adequately. It could be more complete by noting the absence of filtering or ordering, but nothing essential 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?

The tool takes zero parameters, so per the rubric the baseline is 4; there are no parameter semantics for the description to clarify.

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?

States a specific verb ('List') and two concrete resources (published guides, statistics topic pages) plus the returned field (URLs). It implicitly distinguishes itself from search_guides/search_statistics by listing everything rather than filtering, but never names those siblings.

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 only implied: an agent can infer this is for enumerating the full catalogue versus searching, but the description gives no explicit when-to-use or when-not-to-use guidance relative to search_guides, search_statistics, or get_guide.

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 and openWorldHint=false, so the safety profile is covered without prose. The description usefully adds that results include each guide's direct answer and URL, but says nothing about ranking, matching behavior, or result limits beyond the schema.

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

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

Two tight sentences with zero filler; the purpose, audience, and return content are front-loaded. Every clause carries 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?

With no output schema, the description does well to mention the return shape (matching guides with direct answer and URL), and annotations cover the read-only nature. However, it omits the undocumented 'limit' parameter behavior and any hint of how results are selected, leaving a small but real gap for a 2-param search 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 only 50%: 'query' is documented but 'limit' (default 5, max 10) is not described in the schema, and the description adds no information about it. The description gives no syntax, phrasing guidance, or result-count semantics to compensate for the gap.

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

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

States a specific verb (Search) and resource (Shruti Kaushik's research-backed GTM guides) plus the audience (B2B founders) and the topic domains covered. It's clear what the tool does, but it never names or contrasts itself with close siblings like answer_question or get_guide, so an agent can't distinguish them from the description alone.

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 explicit guidance on when to use this tool rather than answer_question, get_guide, or search_statistics, and no prerequisites or exclusions are stated. The intended usage is only loosely implied by the phrase 'for B2B founders'.

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 and openWorldHint=false, so the safety profile is covered; the description adds substantive value by disclosing the shape of returned data (original publisher, date, source URL, host page). It omits any note on result caps or empty-result behavior, but that is a minor gap against a read-only search.

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 filler. The corpus description comes first and the return-value detail second, which is the right front-loading for a search 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?

With no output schema, the description usefully covers what a result contains (publisher, date, URL, listing page), which is the main thing an agent needs to judge citability. A simple two-parameter read-only search is otherwise well served, with only result-count/pagination behavior left unstated.

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 50%: 'query' is documented with examples in the schema while 'limit' is not. The description adds topical grounding that helps formulate the query, but says nothing about the limit parameter's cap or how query terms are matched, so it only partially compensates for the coverage gap.

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

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

States a specific verb (Search) and resource (a library of sourced statistics) and enumerates the content domains covered, so an agent knows exactly what corpus is being queried. It does not explicitly contrast itself with the similarly named search_guides sibling, which is the only missing piece for a 5.

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 only implied by the content domain: an agent can infer this is the tool for finding citable marketing/social statistics rather than prose guides. There is no explicit when-to-use, when-not-to-use, or alternative named against search_guides, answer_question, or get_guide.

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