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

Annotations already declare readOnly/idempotent/non-destructive/openWorld, so the safety profile is covered. The description adds genuinely new behavioral context: the analysis is derived from 'GateOnAI data only' and is explicitly non-prescriptive, which tells the agent how to interpret and caveat the results.

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 core capability (what observations are produced) is front-loaded, and the scope/caveat sentence follows. 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 an output schema present, return values need not be described. The description covers the data source limitation and the interpretive stance, which is what an agent needs before calling. Only the absence of routing guidance versus siblings keeps it from being fully complete.

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 slug list, min of 2 and max of 40, and slug format via example. The description's '2-40 GateOnAI tool slugs' merely restates the schema bounds, adding no syntax or sourcing detail beyond it. 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 names a specific analysis resource – a set of AI tools – and enumerates the three outputs it produces (tools not listed, category overlaps, data connections), which distinguishes it from compare_ai_tools and get_compatible_tools. It stops short of explicitly naming a sibling it is not, so it lands at 4 rather than 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?

It frames the output as 'observations... not recommendations and not judgments', which implicitly tells the agent when this tool is appropriate (neutral gap-analysis) versus a recommendation tool. However, there is no explicit 'use this when X, use Y instead' routing guidance relative to the many siblings like search_ai_tools or find_ai_pipeline.

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 safety profile (readOnlyHint, destructiveHint=false, openWorldHint), and the description usefully adds that nothing is stored on GateOnAI's servers and that the board is embedded in a share link the user can open, clone, and re-share. This extra context justifies the readOnly annotation despite the "build" verb, though it doesn't address idempotency or what a repeat call yields.

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 short sentences, front-loaded with the core transformation and the resulting output. There is minor redundancy between "The user can open the link, clone the board into their own Workbench and share it" and the earlier "a share link that contains the board itself," but nothing is wasted enough to hurt.

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

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

For a single-parameter tool with an output schema and full annotations, the description is essentially complete: it states the transformation, the return shape, the share-link behavior, and the privacy posture. It would be fully complete if it clarified how this differs from the template/pipeline siblings.

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 one parameter at 100% schema coverage, the schema already documents `goal` including an example, so the description need not repeat it. The phrase "a goal described in plain language" adds only marginal framing over the schema, 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 gives a specific verb+resource: turning a plain-language goal into a GateOnAI Workbench board, with clarifying detail about what is produced (steps plus a share link). It reads as clearly distinct from catalog/search siblings, but it never names or contrasts with the closest siblings like get_workflow_template or find_ai_pipeline, so it falls short of 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?

"Use when the user wants a ready-made, visual AI workflow they can open and edit" gives an explicit, actionable condition for invocation. There is no statement of when NOT to use it and no named alternative to route to when the goal is, e.g., merely retrieving an existing template.

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, openWorldHint and destructiveHint=false, so the safety profile is fully covered. The description's remaining content (pricing, features, scores, pros/cons, recommendation) is largely return-value information, and an output schema exists, so it adds little behavioral context such as slug-resolution behavior or failure modes for invalid slugs.

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 the action front-loaded and no filler. The second sentence enumerates return contents, which is somewhat redundant given the existing output schema, keeping it just short of a 5.

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 rich annotations and an output schema present, the definition only needs to establish what the comparison produces and how many tools it accepts, both of which are stated. Nothing essential for correct invocation 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%, so the schema already documents all three parameters including examples. The description only contributes the cardinality constraint ('two or three'), which the schema already encodes via required tool1/tool2 plus optional tool3. Baseline 3 applies when the 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?

States a specific verb and resource ('Compare two or three AI tools head-to-head') and enumerates what the comparison surfaces. It is clear what the tool does, but it never names or contrasts itself against close siblings like get_tool_details, so an agent must infer the boundary.

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

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

The phrase 'compare two or three AI tools head-to-head' implicitly conveys the use case (multi-tool side-by-side rather than single-tool lookup), which is the only guidance present. There is no explicit when-to-use vs when-not, and no alternative sibling is named even though get_tool_details and find_similar_by_philosophy overlap in domain.

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 establish readOnlyHint=true, destructiveHint=false and openWorldHint=true. The description adds genuinely non-redundant behavior: the result is deterministically computed against tagged compatibility data, and multiple ranked pipelines are returned scored on tool quality and path length. It stops short of stating failure behavior when no path exists.

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

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

Purpose is front-loaded in the first sentence and the rest is largely earning its place by establishing trust in the result. The parenthetical about the PostgreSQL recursive engine is implementation detail of marginal value, and the trailing content-type list duplicates the enum, costing a bit of tightness.

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 an output schema present and annotations covering the safety profile, the description supplies what is missing: determinism, ranking criteria, and the shape of the answer (multiple scored alternatives). Only edge cases such as empty-result handling and latency/caching are 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 coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it enumerates the valid content types for start/goal and explains the practical consequence of the step-count parameter ('compare a fast 2-step option against a more thorough 4-step one'). It says nothing extra about free_only or alternatives.

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 a sequence of AI tools that gets you from one type of content to another') with concrete input/output examples (audio→blog post, image→video). It also implicitly distinguishes itself from siblings like get_workflow_template and get_ai_workflow by insisting the result is 'not a guess, a template, or an LLM improvising' but a structurally verified chain.

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 when this tool applies: when you need a tool chain bridging one content type to another, with a fast-vs-thorough trade-off. It gestures at alternatives ('not a template') but never names a sibling tool or states an explicit when-not-to-use condition, so routing still requires inference.

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 readOnly, idempotent, non-destructive, openWorld, so safety is covered. The description adds real behavioral context beyond that: the similarity is computed from MiniLM embeddings of name and tagline rather than shared category, telling the agent why results may differ from taxonomy-based tools. It stops short of describing ranking/scoring or result ordering, so not a 5.

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

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

Three sentences, front-loaded with the core purpose before the contrast. The parentheticals (MiniLM, real input/output matching) are dense but each earns its place by clarifying the distinction; slight density keeps it from a 5.

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 an output schema present and annotations covering the safety profile, the description supplies exactly what is missing: the similarity mechanism and the routing rule versus get_compatible_tools. Nothing needed to call the tool correctly is absent.

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% for both parameters (slug with examples, limit with default/max), so the schema carries the parameter burden. The description adds no syntax, format, or constraint detail beyond what the schema already states, 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?

States a specific verb and resource ('find AI tools conceptually/philosophically similar to a given tool') and immediately names the mechanism, semantic embedding similarity of name and tagline, which distinguishes it from generic category matching. It also names the sibling get_compatible_tools it is not, so an agent can separate the two without opening a 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?

Explicitly routes the agent: 'Use this for tools like X questions, get_compatible_tools for what connects to X questions.' The when-to-use condition and the named alternative are both stated, leaving nothing to inference.

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 readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description usefully adds the mechanism behind the output (GateOnAI's compatibility graph of 4,087,142 tool connections), but says nothing about generation latency, cost, or determinism beyond what annotations provide.

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: the capability first, then the differentiating mechanism. No filler, no restating of the 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?

With a full output schema, complete parameter documentation, and annotations covering safety, the definition is largely self-sufficient. The only real gap is sibling differentiation, which matters given the crowded workflow-adjacent tool set.

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 single query parameter is documented with three concrete examples, so the schema carries the load. The description adds no format, length, or specificity guidance beyond what the schema already states — baseline 3.

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 ("Generate a step-by-step AI workflow") and scopes it to any profession, role, or business task. However, it never distinguishes itself from close siblings like get_workflow_template, build_workflow_board, or find_ai_pipeline, so an agent can't tell them apart from this text 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?

Usage is only implied by the scope phrase "for any profession, role, or business task" and the query examples. There is no explicit when-to-use, when-not-to-use, or named alternative among the several workflow-related siblings, so the agent must infer routing.

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 useful context about the data source (a computed IO-Compatibility Graph) and scale, but says nothing about result ordering, empty-result behavior, or rate limits. With annotations carrying the safety burden, this is adequate but thin.

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?

Front-loaded with the core purpose and clearly delimited from alternatives in the first sentence. The trailing statistic ('4,087,142+ real computed connections') and the 'not a category-similarity guess' defense lean promotional and only marginally aid selection, but the description is otherwise tight.

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 an output schema present, return values needn't be explained, and the description clarifies the non-obvious concept of 'compatible' (IO structural match). It could note behavior on zero matches or how results are ranked, but for a read-only lookup tool the coverage is 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?

Schema coverage is 100%, so both parameters (slug and limit) are fully documented in the schema, setting the baseline at 3. The description adds no syntax, format, or edge-case detail about the slug or limit beyond what the schema already states.

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 ('find') plus resource ('AI tools') and a precise scope ('that genuinely connect', IO-compatibility). It explicitly contrasts itself with category-similarity tools, which cleanly separates it from siblings like find_similar_by_philosophy or search_ai_tools. An agent can tell what this returns 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?

Offers concrete example queries ('what tools work well with ChatGPT', 'what can I feed ChatGPT's output into') that clearly signal the intended use case. However, it never names alternatives or states when NOT to use it (e.g., use search_ai_tools for keyword lookup, find_similar_by_philosophy for thematic similarity), so routing is inferred rather than explicit.

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

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

Annotations already declare this as a safe, read-only, idempotent, open-world query, so the safety profile is covered. The description adds genuinely useful behavioral context beyond them: compliance data is provider-published rather than verified by the service, with an explicit instruction to verify before relying on it — a trust/accuracy caveat an agent needs.

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 compact sentences, front-loaded with the core purpose before the audience guidance and the verification caveat. Every sentence carries information; the only mild excess is the mildly promotional "Essential for..." framing.

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 annotations covering safety, an output schema handling return values, and full schema documentation for all three params, the description covers what remains: purpose, audience, and the data-reliability caveat. Missing only an explicit sibling hand-off for unfiltered searches, a minor gap for a 3-param read 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 100%, with the enum, default, and limit bounds all documented in the schema itself. The description's mention of "GDPR-compliant or EU-hosted" loosely mirrors the 'standard' param values but adds no syntax, format, or filtering nuance 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?

States a specific verb and resource ("Find AI tools") with a clear differentiator scope ("GDPR-compliant or EU-hosted"), which separates it from the generic search_ai_tools sibling. It stops short of naming that sibling explicitly, so the distinction relies on the reader noticing the compliance qualifier.

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 concrete when-to-use context ("European businesses, healthcare, legal, and any use case requiring data sovereignty"), which is more than most definitions offer. It does not, however, state when NOT to use it or point to search_ai_tools for non-compliance-filtered queries.

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 readOnly/idempotent/non-destructive, so the bar is lower. The description adds genuine context beyond them: numbers are computed from live catalog data at request time and are explicitly not predictions or estimates, which tells the agent to treat output as current factual state rather than a forecast.

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?

One front-loaded sentence that leads with the core payload and closes with a scoping disclaimer. It is slightly long because of the 'never a prediction, estimate, or industry-wide claim' clause, but that clause earns its place by bounding the data's authority.

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?

An output schema exists, so return-value explanation is not strictly required, yet the description still previews the metric set, which helps an agent decide whether to call it. With annotations covering safety and the schema covering the sole parameter, the definition is complete enough for a one-parameter read 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 100% and the single 'category' parameter is already documented with slugs and a fallback path (search_ai_tools/browse). The description adds no syntax or format information beyond the schema, 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 names a specific verb and resource (a computed statistical snapshot of one category) and enumerates the payload: tool count, score distribution, top-scoring tools. It distinguishes itself from most siblings, though it does not explicitly differentiate from the closest sibling get_site_stats (site-wide vs category-scoped).

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 when-to-use/when-not-to-use guidance or named alternative for the statistical use case. The only routing hint is inside the schema ('use search_ai_tools or browse to find valid category slugs'), which addresses slug discovery rather than selecting this tool over get_site_stats or compare_ai_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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true, providing a strong safety profile. The description adds no further behavioral context—it does not mention whether results are deterministic, whether the library is updated, rate limits, or any authentication requirements. With annotations covering the basics, the description should still add some operational transparency, but it contributes none.

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 scope. The second sentence about LLM compatibility is somewhat promotional but relevant to usage context. No wasted words overall.

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 a read-only, idempotent tool with a complete schema and an output schema present, the description covers the core purpose but omits practical context such as what the returned prompts look like or how they are structured. It is adequate but leaves gaps for an agent deciding between this and similar prompt-related tools.

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 both parameters are fully documented in the schema, including examples for 'profession' and limits for 'limit'. The description adds no additional parameter semantics beyond what the schema already provides, making a baseline 3 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 (get) and resource (curated AI prompts for a specific profession), with concrete scope (19,715+ prompts, 65 professions). It is clearly distinguishable from siblings like match_prompt_to_task or search_ai_tools, which do not retrieve curated profession-based prompt sets.

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

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

The description implies when to use it—when you want curated prompts for a given profession—but does not explicitly state when to choose this over alternatives like match_prompt_to_task or search_ai_tools. No exclusions or conditions are 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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds only that the figures are 'current live' statistics, which hints at freshness but says nothing about caching, refresh cadence, or cost. Useful but thin beyond the structured hints.

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

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

A single sentence that leads with the verb and payload and then lists the concrete metrics. No filler, no repetition 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?

With zero parameters, an output schema covering the return shape, and annotations covering the safety profile, the description only needs to say what the statistics are. It does so completely.

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 there is nothing for the description to disambiguate; the baseline for a 0-param tool applies. No misleading or missing parameter information.

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 ('Get') and resource ('statistics about the GateOnAI platform') and enumerates the included data points (verified tools, categories, compatibility connections, prompt library size). A reader knows exactly what comes back, though it never contrasts itself against any sibling tool.

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 indication of when to call this versus alternatives such as get_market_landscape or get_trending_tools, and no prerequisites or frequency guidance. The agent must infer that this is a general-purpose overview call.

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 read-only, idempotent, non-destructive, open-world behavior, so the safety profile is covered. The description adds the payload contents (pricing, score breakdown, GDPR status) but conflicts nothing and says nothing about missing-slug behavior or rate limits; the output schema covers the return shape, keeping this at baseline.

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 the action and lookup key front-loaded and no filler. The trailing field enumeration is mildly redundant given a declared output schema, but it remains scannable and does not pad the definition.

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

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

For a single-required-parameter lookup with rich annotations and an existing output schema, the description supplies enough to call it correctly. Only the absence of a 'find the slug first' pointer leaves a small 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?

Schema description coverage is 100% and the single parameter already documents itself with concrete slug examples ('chatgpt', 'midjourney'). The description only restates 'URL slug' without adding format rules such as casing, dashes, or how a slug is obtained, so it earns the baseline.

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 ('Get') and resource ('details about a specific AI tool') plus the lookup key ('by its URL slug'), and enumerates what is returned. It is clearly distinguishable from broad siblings like search_ai_tools or get_trending_tools, though it never names an alternative, so it stops short of the top score.

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: the agent must already possess a URL slug, which hints that search_ai_tools or get_trending_tools precede it. There is no explicit statement of when to use this tool versus compare_ai_tools, find_similar_by_philosophy, or other per-tool siblings, and no 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?

Annotations already declare readOnlyHint, destructiveHint=false, openWorldHint=true and idempotentHint=false, so safety and stability are covered. The description adds useful context that results derive from real user engagement and cover a weekly window, but it doesn't warn that the ranking is time-sensitive/volatile or explain ordering, which would matter for a non-idempotent tool.

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 the core purpose front-loaded and no redundant restatement of the title. The second sentence is mildly expendable but earns some of its place by defining the time window and category breadth.

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

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

For a single-parameter read-only list tool with an output schema (so return shape needn't be described) and full annotation coverage, the description gives enough to call it correctly. Only the lack of sibling guidance and ranking-order notes keeps it from a 5.

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 the sole parameter 'limit' already documents its default, maximum and meaning, so the schema carries the burden. The description adds nothing about the parameter, which is acceptable at baseline.

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 currently trending AI tools'), plus scope ('this week across all categories'), which an agent can distinguish from siblings like search_ai_tools or whats_new by topic. It stops short of explicitly naming how it differs from those discovery-oriented siblings, so it lands at 4 rather than 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: the phrase 'trending... based on real user engagement data' signals a discovery/popularity lookup, but there is no explicit when-to-use, when-not-to-use, or named alternative among the many siblings (whats_new, get_site_stats, search_ai_tools). An agent must infer the selection 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 readOnly, idempotent, non-destructive and openWorld, so the safety profile is covered. The description adds genuinely non-obvious behavior: the template is deterministically ordered, each step is matched by category and GateOnAI Score, and it is regenerated live from current tool data rather than being a static snapshot. That live-regeneration note is the kind of context an agent needs to reason about result 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 tight sentences that front-load what is returned and then handle the sibling differentiation. It is dense but every clause carries information; the hyphenated parenthetical is slightly overloaded but still readable and 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?

An output schema exists, so return-value shape need not be described. The description covers what the artifact is, where it comes from, how ordering is determined, and how it relates to the competing generator tool - sufficient for an agent to call it correctly and interpret the result.

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 the single parameter's description already supplies examples and a fallback path, so the schema does the heavy lifting. The description only restates that the parameter selects 'a specific profession' and adds no slug syntax or format detail beyond the schema. 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?

States a specific verb+resource ('get a pre-built workflow template'), plus scope (2,957+ templates for a specific profession) and the nature of the artifact (deterministic ordered step sequence matched to real tools). It explicitly names sibling get_ai_workflow and contrasts the two, so an agent can disambiguate without opening schemas.

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 this versus the alternative ('curated template for a known profession rather than generating a new custom one from free text - faster and more consistent for common roles'). The schema reinforces the inverse condition: if unsure of the slug, use get_ai_workflow. Both directions of the routing decision are covered.

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, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds real value beyond that: it discloses the retrieval mechanism (BM25 full-text, explicitly not semantic scoring) and what each result contains (prompt text, target tool, profession), which shapes trust in the output.

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?

Front-loaded with the core action and an illustrative example, then a second sentence covering mechanism and return contents. Mostly tight, though the 'verified prompt library' and 'not a semantic guess' phrasing leans promotional rather than purely functional.

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 an output schema present, the description need not enumerate return fields in depth, yet it still summarizes what results contain. Combined with 100% parameter coverage and rich annotations, an agent has everything needed to call this 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 both the task string and the limit range (1-10, default 5) are already documented in the schema. The description adds no syntax or format detail beyond the schema, which is the expected baseline when the schema carries the load.

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 (finds/matches) and resource (existing prompts from a prompt library) with the input modality (free-text task description) and example. It also distinguishes itself from sibling get_prompts_for_profession by being task-driven rather than profession-driven.

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 trigger condition is clear: supply a free-text task description when you want matching prompts. However, it never names an alternative tool (e.g. get_prompts_for_profession) or states when NOT to use it, so guidance stops short of explicit routing.

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, openWorldHint and destructiveHint=false, so the safety and side-effect profile is fully covered. The description adds useful context (the corpus size of 2,901+ verified tools, and that results carry GateOnAI scores and URLs), but does not go beyond that to cover result caps, ranking behavior, or 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 dense sentences with no filler; the headline capability (search 2,901+ tools) is front-loaded and the return payload is summarized second. Slightly compressed to the point of omitting routing guidance, but nothing is wasted.

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 an output schema present, the description need not explain return values, and annotations carry the safety profile; the schema fully documents all six parameters. What remains missing is routing relative to the many sibling search/analysis tools, which is the only meaningful gap for a tool of this complexity.

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 every parameter is already documented in the schema, including enum values and examples. The description restates the filter dimensions (name, use case, category, pricing, GDPR) without adding syntax, defaulting, or combination semantics beyond what the schema provides — 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?

States a specific verb (Search) and resource (GateOnAI's database of 2,901+ verified AI tools) and enumerates the searchable dimensions (name, use case, category, pricing, GDPR). It does not, however, distinguish itself from siblings that also retrieve tools (get_tool_details, compare_ai_tools, find_similar_by_philosophy), so an agent must infer the boundary.

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 'Find tools by name, use case, category, pricing model, or GDPR compliance status,' which tells the agent what inputs are natural. There is no explicit when-to-use vs. when-not, and no pointer to alternatives such as get_eu_gdpr_tools or get_tool_details for a single known 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 already declare this is a safe read (readOnlyHint=true, destructiveHint=false, openWorldHint=true), so the safety profile is covered. The description adds useful context that results are driven by genuine addition timestamps rather than a static list or popularity guess, which explains the non-idempotent time-window behavior. It does not, however, discuss ordering, result caps, or the effect of the days window 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?

Three sentences, front-loaded with the purpose before the optional filter and use-case framing. The 'not a guess or a static list' clause is mildly promotional but does real differentiating work against trend-based siblings.

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 an output schema present, the description needn't explain return values, and it covers the tool's scope, the optional filter, and the time-window model adequately. The only gap is routing guidance among the many sibling tools, which is not strictly required for correct invocation.

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% – both days (1-30, default 7) and category are fully documented in the schema. The description reinforces the category filter with concrete examples ('ai-agents', 'marketing', 'design') and gives a time framing ('this week'), but adds no syntax or behavior beyond what the schema provides, so 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?

States a specific verb and resource ('See real AI tools recently added to GateOnAI') and draws a sharp distinction from a time-based sibling like get_trending_tools by emphasizing genuine addition timestamps rather than popularity. It is clear what the tool returns, though it does not explicitly name which sibling to prefer for loosely related needs.

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 offers implied usage contexts ('staying current on new tool launches', 'checking what's new in a specific space') and mentions the optional category filter, but never states when NOT to use it or which of the many siblings (get_trending_tools, search_ai_tools) is the better choice in a given situation.

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