AppFinder

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

Annotations already declare the tool as read-only, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds valuable context beyond annotations: the data is public and anonymous, covers the last 90 days, and includes both buyer demand shares and how many listed apps answer each need.

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 that leads with the resource and then enumerates the report contents. It is efficient with no filler, though the sentence is long and could be split for faster scanning.

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

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

For a read-only analytics tool with no output schema, the description adequately explains what the report includes: demand shares by feature/integration/security/budget, a 90-day window, anonymity, and app coverage counts. It does not specify a response format, but that is a minor gap for this tool type.

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%: the single parameter 'category' is documented with a clear example in the schema. The description does not add syntax or format details beyond that, so baseline 3 is appropriate.

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

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

The description clearly states the resource (a category's public demand report) and the content it returns (share of buyers asking for each feature, integration, security need, budget, and how many listed apps answer). However, it never explicitly distinguishes this tool from siblings like list_categories or recommend_apps, which keeps it from 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 description explains what the report contains but gives no explicit guidance on when to use this tool versus alternatives such as search_apps, recommend_apps, or list_categories. Usage is only faintly implied by the word 'demand report,' which matches the 2-level calibration for 'no guidance.'

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

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is fully covered. The description adds that the result includes the listing page and that the call is a prerequisite gate; it doesn't describe what a negative answer looks like or edge cases like malformed domains.

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 zero filler; the outcome and the prerequisite ordering are both front-loaded.

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?

No output schema exists, and the description partially compensates by revealing the return content ('and its page'). For a single-parameter read-only check with full annotations, this is nearly sufficient; a brief note on the negative-result shape would complete it.

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

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

Schema coverage is 100% and the single 'website' parameter already has an example ('acme.com'), so the schema carries the semantics. The description adds nothing about accepted formats, domain vs URL normalization, or length limits, which is baseline-appropriate given the coverage.

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 resource and question: whether a website already has a published AppFinder listing, plus its page. It is distinguishable from siblings like search_apps or get_app, though the verb itself comes from the tool name rather than the description.

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?

Explicit sequencing guidance: 'Call this before prepare_listing' tells the agent exactly where this step belongs in a workflow. It does not state when-not to call it or name any alternative for the same check, so it stops short of a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description's added value is disclosing the shape of the result: it compares pricing and returns features/integrations/platforms marked as shared or unique to one app. That return-content disclosure is meaningful, especially with no output schema; it stops short of noting behavior for unknown or ambiguous app names.

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 with zero filler, and the comparison dimensions are front-loaded. The grammar is slightly compressed ("the features, integrations and platforms both apps have or only one has"), but no sentence 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 only two required string params, rich annotations, and no output schema, the main missing piece was return content — and the description supplies the comparison axes and the shared/unique framing. Only edge-case behavior (nonexistent app, ambiguous name, differing result granularity) is left 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% — both 'first' and 'second' are documented as 'app name or slug' with length bounds — so the schema carries parameter meaning. The description adds no resolution guidance (e.g. what happens if a slug is ambiguous or an app is missing), 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?

The description names the operation (side-by-side comparison) and enumerates the compared dimensions — pricing, features, integrations, platforms — plus the shared-vs-unique distinction. That clearly separates it from get_app (single app) and find_alternatives. The verb is elided in a fragment ("Side by side:"), relying on name/title to supply 'compare', so it falls short of a fully explicit verb+resource statement.

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 statement of when to use this versus get_app, find_alternatives, or recommend_apps, and no prerequisites such as both apps needing to exist in the catalog. The use case is inferable from the name alone, but nothing in the text actively routes the agent.

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, openWorldHint=false and destructiveHint=false, so safety is covered. The description adds real value by disclosing the two result streams (same-category apps vs. what replacement buyers chose) and the ranking rule, but it gives no sense of result volume, pagination, or how the two streams are distinguished in the response.

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 compact sentence with the ranking rule placed up front. It is efficient and free of filler, though the sentence fragment construction makes it slightly harder to parse than a plain declarative.

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

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

With no output schema, the description carries the burden of explaining the return value, and it only sketches it: two loosely described groups with no statement of count, limits, or field shape. For a simple one-parameter read tool this is adequate but leaves real gaps.

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?

Only one parameter exists and the schema describes it fully at 100% coverage ('The app to replace, e.g. Jira'). The description adds nothing beyond that framing, so the baseline 3 for high-coverage schemas 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 conveys the resource (apps similar to a given app) and even the ranking logic ('same category, most shared features first'), but it is a noun phrase rather than a verb+resource statement, and it never explicitly says 'find alternatives to the supplied app'. Against siblings like recommend_apps and compare_apps, the boundary is only implied.

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 when-to-use or when-not-to-use guidance and no named alternative. An agent cannot tell from the text whether to call this instead of recommend_apps or compare_apps, even though those siblings clearly overlap.

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 fully covered. The description adds value by enumerating the returned data fields, which compensates for the absence of an output schema. Minor gaps: no pagination or rate-limit context.

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

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

A single front-loaded sentence that lists the returned fields without waste. Every clause earns its place.

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

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

For a read-only single-resource fetch with no output schema, the description adequately conveys the return contents and annotations cover safety. It lacks only minor details like error handling for unknown apps or pagination, so not quite 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?

With a single parameter at 100% schema coverage, the schema already documents that 'app' accepts a name or AppFinder slug with an example. The description adds nothing about the parameter, so baseline 3 is appropriate.

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

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

The description states the specific resource (one app's listing) and enumerates its contents (description, categories, pricing, platforms, features, integrations, rating, facts with sources). Combined with the title 'Get an app', an agent can distinguish it from search_apps or compare_apps. It's clear but doesn't explicitly name a sibling for differentiation.

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 explicit when-to-use guidance or alternatives are given. The single-app-listing framing implies it's for fetching one app's details, but the agent must infer when to prefer this over check_listing or search_apps.

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 safety behavior is covered. The description usefully says the result includes app counts per category, but it does not describe ordering, pagination, or whether counts are totals or filtered counts.

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

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

It is a single short sentence fragment with no wasted words. The key information — categories and app counts — is front-loaded.

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 parameterless, read-only listing tool with strong annotations, the description provides enough context about what is returned. The absence of an output schema is partly compensated by the mention of categories and app counts, though ordering and count semantics remain unspecified.

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

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

The tool has zero parameters, and the schema is empty. Per the baseline for parameterless tools, no additional parameter semantics are needed from the description.

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

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

The description identifies a specific resource (categories) and adds a meaningful scope detail (how many apps each lists). It does not explicitly differentiate this from category_demand, but the resource and returned data are clear enough for an agent to distinguish it from sibling 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?

There is no guidance on when to use this tool instead of siblings such as category_demand or search_apps. The name implies a browse/list use case, but the description provides no explicit selection criteria or 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 only declare the generic mutation profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true). The description adds substantial context beyond that: nothing is published until human approval, the maker reviews and submits personally, listing is free, and a link is returned. It does not address repeat invocations (idempotentHint=false) or failure modes, which keeps it at 4.

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: audience and action first, then what happens to the link, then the approval/free constraints, then the input rule. Zero filler and the most decision-relevant facts are front-loaded.

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

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

Despite no output schema, the description says exactly what is returned ('a private link') and what the caller must do with it. With all 12 parameters documented in the schema and the side-effect/approval flow explained, 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 the baseline is 3, but the description adds real guidance the schema cannot: 'Pass only what the maker said; leave out what you don't know (the website fills the gaps).' That tells the agent to omit rather than fabricate fields, which is a meaningful semantic instruction for all 12 parameters.

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

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

States a specific verb and resource ('fills in AppFinder's submission form... returns a private link') plus the audience ('for a maker who wants to list their own app'). This is clearly distinguishable from every sibling, which are all read/search tools (search_apps, get_app, recommend_apps, check_listing).

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 opening clause gives the triggering condition ('For a maker who wants to list their own app'), which is enough to select this over the search/discovery siblings. It does not name an explicit alternative or when-not-to-use case, so it falls short of a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false, so the safety profile is covered. The description adds genuine value beyond that by disclosing the response shape: per-pick cited facts (fits, gaps, ruled-out must-haves) plus a follow-up question, which tells the agent what to expect back without an output 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?

A single front-loaded sentence that packs the action, the input, and the output contract with no filler. Every clause earns its place.

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

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

With no output schema, the description does the work of explaining the return contents, and annotations cover the safety profile. Minor gap: it doesn't say how many recommendations come back or what happens when nothing matches the must-haves.

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?

Only one parameter, fully documented in the schema with a concrete example. The description's 'plain-language request' framing adds a useful hint that free-form natural language, including constraints like SSO or price, is expected rather than keywords.

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 ('Ranks') and resource ('apps in the AppFinder directory') with the input shape ('plain-language request'). This clearly separates it from keyword-oriented siblings like search_apps and from compare_apps/find_alternatives, which don't produce ranked recommendations with rationale.

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 'plain-language request' implicitly signals the natural-language recommendation use case versus keyword search, but the description never names an alternative or states when-not to use it. No prerequisites or exclusions are given, leaving sibling routing 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 readOnlyHint, idempotentHint, non-destructive behavior, and closed-world scope. The description adds useful behavioral context that results are ordered by relevance, but it does not cover pagination or matching semantics 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 text. It efficiently communicates the search scope and result ordering.

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

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

For a simple read-only search tool with full schema coverage and annotations, the description is nearly complete. It could do slightly more to clarify when this search is preferable to sibling discovery tools, but nothing critical is missing 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%, with all three parameters documented, including query examples and the category reference to list_categories. The description adds that matching can occur by name, keyword, or job, but does not add syntax or filtering semantics beyond the schema.

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

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

The description states a specific verb and resource: full-text search of the directory. It also names the searchable fields (name, keyword, job) and ranking behavior. It does not explicitly distinguish itself from siblings like find_alternatives or recommend_apps, 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?

The description implies usage by stating what can be searched and that results are relevance-ranked. However, it gives no explicit guidance on when to use this versus find_alternatives, recommend_apps, or category browsing via list_categories.

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