NutsGrowth

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

Annotations already cover the safety profile (readOnlyHint, openWorldHint=false), so the bar is lower, and the description usefully adds the exact return payload (price/terms, last checked, country, open source, self-hostable, reviews, positioning) plus the 2-4 cardinality constraint. It omits any note on failure behavior for unknown slugs 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 sentences, front-loaded with the core behavior and the returned fact list, with no filler. The long enumerations are dense but they substitute for a missing output schema, so they earn their 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 no output schema, the description correctly carries the return-value burden by listing every compared field. Cardinality and required input are covered by the schema, so the only residual gap is the absence of error/edge-case guidance.

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% and the schema already documents the slugs array, so the baseline is 3. The description adds genuine value beyond the schema by specifying slug provenance (search_tools, find_alternatives) and that exact names are also accepted.

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 (compare two to four listings side by side) and even enumerates the exact fields returned for each listing. It is clearly distinguishable from siblings like get_tool (single lookup) and search_tools (broad search), which the description names as slug sources.

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

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

Usage is implied by the cardinality and comparison framing, and it tells the agent where slugs come from (search_tools, find_alternatives, or exact names). It never states when to prefer this over get_tool for a single item or when comparison is inappropriate.

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 cover the read-only, closed-world safety profile, so the description's added value is its disclosure of matching semantics ('matching is on the product's name as the directory writes it'), the max-10 result cap with total, alphabetical ordering, and the suggested-names fallback when nothing matches. Those are genuine behavioral traits beyond the annotations, though nothing is said about pagination or rate limits.

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

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

Two dense sentences with no filler; the resource and scope lead, followed by matching rules and return shape. The embedded examples ('Notion', 'Google Drive') earn their space by pinning down expected input format.

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 still supplies the return contract: at most 10 listings plus a total, alphabetized, with a documented fallback response. Combined with the 100%-covered input schema and annotations, an agent has everything needed to call it correctly.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning for the required 'product' parameter by explaining the exact-match rule against the directory's own spelling and warning about the no-match fallback. It also clarifies that the remaining filters behave identically to search_tools.

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 ('List what the NutsGrowth directory files as a replacement for a named product') and anchors it against the sibling search_tools by sharing its filter set. An agent can distinguish this from search_tools and compare_tools immediately.

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?

Gives clear context: use it when you have a named product to replace, and it explicitly defers filter semantics to search_tools. It also describes the empty-result fallback behavior. It stops short of stating when NOT to use it versus search_tools or get_tool.

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

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

Annotations cover the safety profile (readOnlyHint=true, openWorldHint=false), so the description's remaining burden is interpretability, and it delivers: it warns that positioning flagged written_by "vendor" is a company claim rather than a directory finding. It does not cover error behavior for an unknown slug or data freshness beyond 'last checked'.

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 payload description and closing with the one interpretive caveat that matters. The field list is dense but each item is concrete; nothing is padding, though the first sentence runs long as an enumeration.

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 return-shape burden and does so by enumerating the fields (identity, location, licensing, price, last-checked, review count/average, positioning). An agent can call this correctly and interpret the result; only error/failure behavior is unaddressed.

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% and there is a single required parameter with its length bounds and sourcing instructions already in the schema. The description adds no syntax or format detail beyond that, so the baseline 3 applies.

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

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

The description states a specific verb (read) and resource (one listing) and enumerates the returned fields, so the agent knows exactly what this tool produces. It does not explicitly name the sibling tools it competes with, but 'one listing' versus search/compare/list is clear from the name and title.

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: fetch detail once you already have a listing identity. The slug parameter description does route the agent to search_tools or find_alternatives as sources for the slug, which is useful indirect guidance, but there is no explicit when-to-use versus when-to-prefer-compare_tools or find_alternatives.

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 cover the read-only, closed-world safety profile, and the description adds real context on top: the return shape (values plus listing counts) and the non-obvious semantic that open_source and self_hostable are standalone filters rather than tags. That last point prevents a plausible misuse and is not derivable from 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 sentences, both front-loaded with the substantive content and zero filler. The first sentence is dense (purpose plus consumer) but each clause carries information, and the caveat sentence is short and load-bearing.

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 input parameters, no output schema, and safety covered by annotations, the description supplies what an agent actually needs: what categories of values come back, that counts accompany them, and the one semantic trap to avoid. A brief note on pagination or result size would close the remaining gap.

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

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

The tool takes zero parameters, so the baseline is 4 and there is nothing further the description needs to disambiguate about arguments.

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?

Names a specific resource (categories, countries, tags in use in the directory) and the payload each carries (listing counts). It also positions itself relative to siblings by naming search_tools and find_alternatives as the consumers of its output, so an agent can tell it apart from get_tool or compare_tools.

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

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

States the use case explicitly: fetch these values so search_tools and find_alternatives 'can be given values that exist'. That is a clear when-to-use condition, though it doesn't spell out exclusions or ordering constraints relative to the other 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered; the description adds genuinely new behavior: all words must appear, results are capped at 10 with a total, and price provenance is human-checked with an explicit warning not to substitute an external price. Return-shape detail beyond the annotations is useful, though pagination/offset behavior for retrieving beyond 10 results is absent.

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 dense but front-loaded sentences: purpose and matching first, the filter precondition, then the return contract. Every clause carries information, though the third sentence is long enough that the return-format details could be split for scannability.

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 the work of describing returns (at most 10 listings plus total, each with NutsGrowth page, product website, human-checked price, and last-checked date). Combined with the matching and precondition rules, an agent can call this correctly; only retrieval beyond the 10-item cap is unexplained.

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 baseline is 3, but the description adds meaning the schema cannot express: which fields each query word is matched against, AND semantics across all supplied filters, and the cross-parameter rule that at least one filter must be present. That is real added value over the per-parameter schema text.

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 ('Find startups and SaaS tools in the NutsGrowth directory') and defines the matching scope precisely (name, tagline, category, country, price, replaced products, tags). It does not explicitly distinguish itself from siblings like find_alternatives or compare_tools, so an agent must infer the boundary, but the core purpose is 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?

Gives a hard precondition ('Give at least one of query, category, country, tags, open_source, self_hostable or free_plan') and points to list_filters as the source of valid category/tag/country values, which is actionable usage guidance. It stops short of saying when to prefer a sibling (e.g. find_alternatives) over this tool.

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

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

Annotations only mark this as a non-destructive, idempotent write; the description adds what the annotations cannot — that a human reviews every suggestion, that turnaround is days, and that resubmitting an already-queued URL returns its arrival time (which is exactly why idempotency holds). It also discloses the handling of the optional email and the consent requirement before sending, which meaningfully de-risks an agent-initiated write.

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?

Four sentences, each doing distinct work: identity/equivalence, review latency, duplicate outcomes, and input discipline plus consent. It is front-loaded with the action and contains no restatement of the tool name or 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?

For a 15-parameter write with a nested price object and no output schema, the description covers the important unknowns: cost of the action (human review, days), duplicate behavior, and consent. It does not describe what a successful first-time submission returns, which is the one gap an agent might notice after calling.

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

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

Schema description coverage is 100%, so the field-level semantics (tag slugs from list_filters, pricing_url required with price, USD-only amounts, character caps) are already carried by the schema. The description only adds sourcing policy — take values from the product's own site and leave gaps blank — without per-parameter detail, so the baseline of 3 is correct.

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 a specific verb and resource — 'Send a startup or SaaS product to the NutsGrowth review queue' — and anchors it to the equivalent public form at /submit. That makes it immediately distinguishable from the sibling read tools (search_tools, get_tool, compare_tools, find_alternatives, list_filters), all of which look up rather than submit.

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

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

It gives concrete operating guidance: fill in only what the product's own site supports, omit the rest, and ask the person before sending. It also handles the duplicate case by explaining that an already-listed URL returns the listing page and claim path. It stops short of explicitly telling the agent to check get_tool first, so the routing is implied rather than spelled out.

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