aigovernancetools

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered structurally. The description still adds genuine value with the "published only" scope and the explicit exclusion "Exam questions and answers are never returned" — a meaningful data-boundary disclosure. It stops short of describing behavior on missing inputs or error outcomes.

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, front-loaded with the action and resource, followed immediately by the data-exclusion caveat. Nothing redundant or padded.

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 tool with full annotations and no output schema, the description adequately covers what is returned and what is deliberately withheld. The only gap is behavior for a nonexistent or unpublished slug, which an agent would want to know.

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 slug parameter already carries a pattern, length bounds, and description. The description only restates lookup-by-slug and adds the published-only filter implication, which is close to the baseline for a fully documented schema.

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

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

States a specific verb and resource ("Get one published academy course by its slug") and even enumerates the payload contents (objectives, lessons, FAQ). The singular "one ... course" implicitly contrasts with the listing siblings such as list_courses, but no sibling is actually named, so it falls just 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?

The word "published" signals that only live courses are retrievable, which is a partial when-to-use constraint. There is no explicit guidance on when to prefer this over list_courses or a search sibling, nor what to expect for an unpublished or unknown slug.

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, closed-world, non-destructive semantics, so the safety profile is covered. The description adds one real behavioral fact beyond that — only published tools are retrievable — but says nothing about not-found behavior or what a miss returns.

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 short sentence, no filler, with the distinguishing qualifier ('published') front-loaded before the lookup key. Nothing to trim.

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 tool with rich annotations and a fully documented schema, the description covers what is needed to select and call it. The main omission is not-found/error behavior, which no other field supplies since there is no output schema.

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

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

Schema description coverage is 100% and the single slug parameter is fully constrained (pattern, length bounds) in the schema itself. The description only restates 'by its slug' and adds no syntax or format guidance beyond the schema; 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 (Get) and a specific resource (one published AI governance tool) with the retrieval key (slug). The 'published' qualifier scopes the result set meaningfully. It does not explicitly differentiate itself from the closest sibling, search_listings, but the singular-vs-plural distinction makes the split inferable.

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 'by its slug' — the caller must already know the exact slug, which suggests this is the direct-lookup path rather than the discovery path. No explicit statement of when to prefer it over search_listings or what to do when the slug is unknown.

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 genuinely new information by stating the response includes the product's page and outbound link, which matters because there is no output schema. It omits not-found behavior, which keeps it below 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?

A single front-loaded sentence with no filler; the lookup key and the returned content are both stated in one pass.

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 tool with full annotation coverage and complete schema documentation, this is nearly sufficient. The only real gap is what happens when the slug does not resolve, which an agent would want to know.

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 slug parameter carries its own description plus a pattern, so the schema does the heavy lifting. The description's 'by its slug' restates rather than extends that, matching the 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 and resource ('Get one affiliate product') and scopes it to a single-item lookup, which implicitly contrasts with the plural sibling list_products. It does not explicitly name the alternative tool, so it stops 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?

'by its slug' implies the precondition that the caller already has a slug, which is useful context for choosing this over list_products or search_listings. However, no explicit when-to-use/when-not statement or named alternative appears.

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, non-destructive and closed-world, so safety is covered. The description still adds useful content beyond that: it discloses that the payload is aggregate counts plus a data-freshness timestamp, which tells the agent what kind of result to expect from a zero-param call.

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 sentence, front-loaded with the verb and the counted entities, then closing with the freshness detail. Nothing is wasted and there is no filler.

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

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

For a zero-parameter, read-only aggregate tool with no output schema, the description covers what is returned at a conceptual level (three counts plus a last-changed timestamp). It stops short of describing the exact response shape or key names, which is the only remaining gap for an agent wiring up a call.

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 no parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. It correctly omits any parameter talk rather than inventing 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?

The description gives a specific verb (get) plus the exact resource: aggregate counts of published tools, places and categories, together with a last-changed timestamp. The phrase 'the number of' cleanly marks it as an aggregate/count tool as opposed to the sibling listing tools (get_listing, list_categories, list_places), though no sibling is named 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?

Usage is only implied: the agent can infer it should call this when it needs counts or freshness rather than actual records. There is no explicit 'use this instead of list_*' or 'do not use for full listings' guidance, and no preconditions are stated.

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

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

The annotations already declare read-only, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds value by stating that results include tool counts, but it does not elaborate further on output shape or ordering.

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

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

The description is a single front-loaded sentence with no filler. Every part of the sentence contributes to understanding what the tool returns.

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, read-only listing tool with rich annotations and no output schema, the description is nearly complete. It states the resource and the key returned data ('tool counts'), though it could mention ordering or output format for full completeness.

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

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

The tool takes zero parameters, so there are no parameter semantics to explain and the baseline score applies. The description does not need to compensate for any schema gaps.

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

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

The description uses a specific verb and resource ('List categories') and adds a meaningful scope detail ('with their AI governance tool counts'). It does not explicitly distinguish this tool from siblings like list_places or list_sources, but the resource is clear enough to identify the operation.

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

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

The description gives no explicit guidance about when to use list_categories versus alternatives such as get_stats, list_places, or list_sources. Usage is only implied by the tool name and the simple zero-parameter listing behavior.

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, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds two genuinely new facts beyond that: only published courses are returned, and each record includes level, length, pass mark and certificate validity. It does not mention ordering or what happens at the limit boundary, so it adds context without being rich.

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 sentence, front-loaded with the verb and scoped resource, then the returned fields. Every clause earns its place and there is no redundant restatement of the name.

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, zero-required read tool with complete annotations, the definition covers scope (published only) and return fields, which matters because no output schema exists. The remaining gap is result ordering and whether the 50-item cap implies truncation, which an agent might want to know before listing.

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 level enum and limit bounds (1-50, default 50) fully documented in the schema. The description's mention of 'level' marginally reinforces the filter but adds no syntax, default, or pagination semantics 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?

States a specific verb (List) and a precisely scoped resource (published academy courses), plus the fields each result carries. It is clearly distinguishable from get_course (single item) and the unrelated list_* siblings, though no sibling is named 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?

Usage is only implied: the agent can infer this is the browse-all path as opposed to get_course for a single course, and that the optional level parameter narrows results. There is no explicit 'use this when' or exclusion statement, and nothing about what to do if no courses match.

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 openWorldHint=false, so the safety profile is fully covered. The description adds the useful filter condition (only places that actually have tools), but says nothing about ordering, return shape, or whether counts are included. No contradiction with annotations.

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

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

A single front-loaded sentence with no filler. Every word (the verb, the three geography levels, and the AI-governance filter) carries meaning.

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 facet tool with no output schema, the description conveys enough to invoke it correctly. The only real gap is that the return format (plain names vs. names with counts) is unspecified, which matters slightly more since no output schema exists.

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

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies a no-argument enumeration rather than a filtered query.

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 ('List') and a precise resource ('countries, regions and cities that have AI governance tools'), making the scope and the filtering condition clear. It is distinct from siblings like list_categories and list_sources by naming the domain (geographies) explicitly, though it never names a sibling to contrast with.

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 versus the analogous facet tools (list_categories, list_sources) or search_listings. The agent must infer that this is the geographic facet lookup. No prerequisites or exclusions are stated.

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, non-destructive and closed-world, so the safety profile is covered. The description's contribution beyond that is that results include product counts — useful, but no ordering, pagination, or empty-result behavior is disclosed.

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 short sentence with zero filler, and the key payload detail (counts) is front-loaded where the agent will read it.

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, read-only listing tool with full annotation coverage, the description plus schema is enough to invoke it correctly. Minor gap: with no output schema, the exact return shape (field names, ordering) is only alluded to.

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 no parameters, so there is no parameter semantics to explain; the baseline for a zero-param tool 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 ('List the product categories') plus a distinguishing payload detail (product counts). However, it does not differentiate itself from the sibling 'list_categories', leaving an agent to guess which category lister to call.

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, or alternative-tool guidance. The only implied cue is that this variant returns counts, which the agent must infer rather than being told.

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, closed-world behavior, so the safety profile is fully covered. The description adds one genuinely non-obvious data semantic — the price is a point-in-time snapshot from the day the product was checked, not a current price — but says nothing about result volume, ordering, or pagination.

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, zero filler, with the core action and the most decision-relevant detail (price snapshot semantics) front-loaded ahead of the filter note.

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-optional-param, read-only list tool with full schema coverage and annotations, the description is largely sufficient and even hints at the returned fields (product + price). The one gap is the absence of any output schema combined with no note on pagination or result limits.

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

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

Schema description coverage is 100% and the single optional parameter carries its own type, slug pattern, and length bounds. The description only restates that the parameter filters by category, adding no syntax or behavior 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?

States a specific verb and resource ("List the affiliate products") plus the returned payload (price as of the check date) and the filter dimension. It implicitly separates from get_product (single record) and list_categories, but never names a sibling, so the differentiation is left to inference.

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?

"Filter by product category" plus the optional parameter implies narrowing usage, but there is no explicit when-to-use guidance and no mention of when to prefer get_product, list_product_categories, or list_categories instead. Usage is implied rather than stated.

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, destructiveHint=false and a closed-world scope, so safety is covered. The description adds real value beyond them by disclosing the shape of each returned record (license and refresh per source), which no output schema provides.

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 sentence, front-loaded with the verb and resource, with the returned fields appended where they add the most value. Nothing is wasted and nothing is buried.

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 no-parameter, closed-world list with no output schema, the description covers both what is enumerated and what each entry contains. A note on ordering or whether the source list is fixed would close the last 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 there is nothing to disambiguate and baseline 4 applies; the description correctly makes no parameter claims.

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?

Specific verb ('List') plus resource ('where the AI governance tool data comes from') and the fields returned (license, refresh). It reads clearly against siblings like get_stats and list_places, though it never names them to reinforce separation.

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 provenance/attribution lookup, but there is no statement of when to prefer it over get_stats or search_listings, nor any prerequisite or exclusion.

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, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds the useful scope constraint that only 'published' tools are searched, but it does not discuss pagination behavior, empty results, or fallback matching beyond what the schema already states.

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

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

The description is a single front-loaded sentence with no wasted words. It states the core action and scope 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?

Given eight optional parameters, full schema descriptions, and read-only annotations, the description provides the minimum needed to understand the tool's core purpose. However, it omits routing guidance among siblings and does not describe return or pagination behavior, leaving meaningful gaps for an agent choosing and invoking the 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%, so all eight parameters are already thoroughly documented in the input schema. The description echoes the text, place, and category facets, but adds no meaning beyond what the schema provides for q, city/region/country, category, sort, limit, or cursor.

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

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

The description uses a specific verb (Search) and resource (published AI governance tools), and names the search facets: text, place, and category. It distinguishes this plural search operation from get_listing and the list_* siblings, though it does not explicitly name those alternatives.

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 phrase 'Search ... by text, place and category.' There is no explicit guidance on when to use this versus get_listing, list_categories, list_places, or list_sources, and no prerequisites or exclusions are stated.

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