AIWiseRank

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 meaningfully by disclosing what the report actually contains (takeaways, risers/fallers, category momentum) and the default-month behavior, which is beyond what the annotations state. It stops short of describing output format or size, which matters somewhat since there is no 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?

Two sentences, no filler, and the substantive content list comes first with the default behavior trailing. It reads as terse fragments rather than full sentences, but 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?

For a read-only report tool with one optional parameter, no nested objects, and no output schema, the description covers purpose, contents, and default behavior adequately. Describing the return payload's structure is the only mild gap, and the content enumeration largely compensates for the missing 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?

With one optional parameter at 100% schema coverage, the baseline is 3, but the description adds a genuine fact the schema omits: that omitting 'month' defaults to the latest month. That resolves the main ambiguity an agent would have about calling the tool with zero 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 names the resource (a monthly AI traffic report) and enumerates its contents — key takeaways, biggest risers/fallers, category momentum — so an agent knows it returns a synthesized trend summary rather than raw tool data. It doesn't explicitly contrast itself with siblings like best_ai_tools or compare_ai_tools, and the phrasing is a noun clause rather than a verb+resource, but the intent is unambiguous.

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

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

"Defaults to the latest month" is the only usage signal, and it addresses the parameter rather than the decision of when to pick this tool. Nothing states when to prefer this over best_ai_tools or compare_ai_tools, so the agent must infer that this is the recurring/monthly-summary option. Usage is implied by the 'month' framing, not 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 cover safety (readOnly, idempotent, non-destructive, closed-world), so the description adds value by disclosing the ranking method and the returned fields (traffic, pricing, links). It omits how many results come back by default and that the list is capped, which matters since no output schema 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?

Two sentences, front-loaded with the core purpose and ranking basis, followed by the category list. The long enumeration is verbose but genuinely functional for choosing a valid value.

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 two-parameter lookup with no output schema, the description supplies the ranking logic and locality of returned data. The main gap is result-count/pagination behavior for the limit parameter.

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 only 50%, and the description merely repeats the category enumeration already present in the schema rather than adding constrained meaning. The limit parameter's behavior (default 10, max 25) is left entirely to 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?

States a specific resource (AI tools in a category) and an explicit ranking basis (real web traffic) plus returned fields (pricing, links). It does not explicitly distinguish itself from close siblings like search_ai_tools or compare_ai_tools, which the agent must infer.

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 'in a category' — the agent can infer this is a browse-by-category entry point, but there is no statement of when to prefer it over search_ai_tools, compare_ai_tools, or list_ai_tool_categories.

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 readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description goes further by enumerating what the comparison returns (traffic rank, trend, pricing, free plan, score, alternatives), which matters because there is no output schema. It still omits what happens when a supplied tool name isn't found.

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 names the operation, the constraint, and the returned dimensions with no filler. Nothing could be removed without losing 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?

For a one-parameter read-only tool with no output schema, listing the returned metrics makes the description essentially self-sufficient. The only meaningful gap is error/partial-match behavior for invalid tool names.

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 100% schema description coverage and the minItems/maxItems bounds already encoded in the schema, the description adds little beyond restating the 2–4 range. The example format lives in the schema, not the description, so this is baseline territory.

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 (side-by-side comparison) and resource (AI tools), and scopes it with a count range of 2 to 4 and the exact data points compared. It implicitly distinguishes itself from single-tool siblings like get_ai_tool, though it never names an alternative 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 '2 to 4 tools' framing signals 'use this when comparing multiple tools at once.' There is no explicit statement of when to prefer this over search_ai_tools or get_ai_tool, and no mention of prerequisites or failure modes (e.g., unknown tool names).

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 real value by enumerating exactly what the response contains (rank, 7/30-day trend, pricing/free plan, score, alternatives, links), which substitutes for the absent output schema. It stops short of stating behavior for a name that doesn't match a known 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?

One front-loaded sentence led by the scope ('one AI tool'), followed by a compact field list. There is no filler and 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 one-parameter lookup with no output schema, listing the returned fields is the key missing piece and the description supplies it. The remaining gap is edge-case behavior (no match, ambiguous name), which matters little for a read-only idempotent 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?

Schema coverage is 100% and the parameter carries its own description with concrete examples (Cursor, Midjourney, ElevenLabs), so the schema does the work. The description only reinforces that a single tool is addressed; it adds no matching semantics (exact vs fuzzy, case sensitivity) 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 single-resource lookup clearly: 'Everything AIWiseRank knows about one AI tool', with an enumerated payload (summary, ranks, trend, pricing, score, alternatives, links). It is specific enough to act on, but it never names the sibling lookups (search_ai_tools, compare_ai_tools) that could be confused with a single-tool detail fetch.

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: 'one AI tool' signals a single-record lookup keyed by name, which contrasts weakly with search_ai_tools and best_ai_tools. There is no explicit when-not-to-use clause, no statement that this is the detail call after a search, and no guidance for unknown tool names.

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 establish that this is a read-only, idempotent, non-destructive, closed-world operation. The description adds useful detail about the returned content (tool counts and most-visited tool), but does not describe ordering, pagination, or how 'most-visited' is determined.

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 conveys the resource and the key returned fields efficiently.

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, read-only listing tool with no output schema, the description gives enough context about what will be returned. Minor gaps remain around ordering and the precise meaning of 'most-visited', but they are unlikely to prevent 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?

The tool has zero parameters, so the baseline is 4. There are no parameter semantics that the description needs to clarify.

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

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

The description clearly identifies the resource: AI tool categories ranked by AIWiseRank, including tool counts and a most-visited tool. It distinguishes this category-listing tool from the sibling tools that operate on individual AI tools, though it uses a noun phrase rather than an explicit verb.

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 guidance about when to call this tool versus alternatives such as search_ai_tools or best_ai_tools. There are no exclusions, prerequisites, or suggested workflows.

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 context: units, token direction (input/output), provider scope, and the 'where published' caveat implying some benchmark scores are missing. It does not disclose the default 15-row cap or that results are truncated, which matters behaviorally.

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: first establishes the resource, units, and coverage; second states the sorting options. No filler, and the most decision-relevant information (what data, which sort modes) 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 read-only, no-output-schema lookup tool with three optional parameters, the description covers what data is returned and from which providers. The remaining gap is result shape and the limit/truncation behavior, but nothing critical to invoking it correctly 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 coverage is only 33% — only 'provider' carries a schema description — so the description should compensate more. It does explain the three sort modes in plain language, adding a little value over the bare enum, but it says nothing about 'limit' (default 15, max 50) or how truncation interacts with sorting.

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 highly specific resource: USD per 1M input/output token prices for named providers, plus ARC-AGI-2 and SWE-bench Verified scores. The data domain is clearly distinct from the sibling tools, which all deal with AI tool listings rather than model pricing, though the description never explicitly contrasts itself with them.

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 closing sentence ('Sort by cheapest, best ARC-AGI-2 or best SWE-bench') implies the main usage patterns, and the pricing/benchmark framing implies when this tool is relevant. However, there is no explicit when-to-use vs. when-not guidance and no mention of alternatives among the ai_traffic_report / compare_ai_tools siblings.

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

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

Annotations already declare read-only, idempotent, closed-world behavior, so the description's job is to add context beyond safety. It does that by disclosing the ranking basis (real web traffic) and the return payload (pricing, rank, AIWiseRank link), which matters since there is no output schema. It doesn't mention result limits or how the default limit of 8 behaves.

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 clauses: what it finds (with examples), what filters exist, and what comes back. Front-loaded with the core purpose and no filler sentences.

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 five parameters, high schema coverage, and no output schema, the description supplies the missing return-value information and the ranking rationale an agent needs to decide between this and siblings. Only minor gaps remain, such as the limit parameter and result volume.

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 80%, so the schema already documents query, category, free_plan_only, and max_monthly_usd with good specificity. The description only restates those filters in shorthand and adds nothing about semantics (e.g., how budget interacts with free plans, already covered in schema), and never mentions the limit parameter. 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") plus the differentiating detail that results are ranked by real web traffic, with concrete example queries. It is clear what the tool does, though it does not explicitly distinguish itself from siblings like best_ai_tools or compare_ai_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?

The description gives clear trigger context via natural-language need examples ("edit podcast audio", "SEO keyword research") and lists the optional narrowing filters. It stops short of naming when to prefer a sibling tool or when this tool is the wrong choice.

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