The AI Tools Index

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

Annotations exist (readOnlyHint: false, openWorldHint: true) and the description builds on them meaningfully. It discloses the timing of side effects ('nothing is charged until a person completes checkout in a browser'), adds the error-condition profile, and clarifies the return value's purpose. This goes beyond the annotations without contradicting them, though it could have briefly addressed non-idempotency given idempotentHint: false.

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 three sentences and every clause earns its place: purpose first, then a critical caveat about the human-completed purchase, then error conditions. It is front-loaded, actionable, and free of fluff. Perfectly sized for an agent to parse quickly.

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 tool with only 2 parameters, an enum, annotations, and an output schema, the description covers all essential behavioral gaps: what it returns (a URL for a human), what it does NOT do (charge immediately), and when it fails (three explicit conditions). The output schema handles return-value documentation, so no further description is needed.

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 rich inline parameter documentation: slug has an example and pattern, product has an enum and a reference to list_offers. The description does not add parameter-specific semantics, but with full schema coverage, the baseline of 3 is appropriate. No additional burden falls on the description here.

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+resource construction: 'Create a Stripe checkout URL for one paid add-on on one listing, and return it for a HUMAN to open.' This clearly differentiates the tool from siblings like list_offers (which presumably enumerates offers) by emphasizing the human-in-the-loop checkout flow and that no purchase is completed. The scope is precise and immediately actionable.

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 provides clear context on when to use: when a human needs to complete a purchase of a paid add-on. The imperative 'Never present this as a completed purchase' gives an explicit usage guardrail, and the error conditions (listing missing, add-on not purchasable, payments disabled) help the agent decide when to call. However, it does not name an alternative tool to use instead, though the schema's cross-reference to list_offers partially fills that gap.

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 declare readOnlyHint, openWorldHint, and idempotentHint, but the description adds behavioral details: it returns null for non-resolving slugs, indicating error handling, and clarifies the data source and scope (public detail). This goes beyond the annotations by describing the return value in edge cases and the nature of the data (revenue signals, for-sale status).

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 two sentences, front-loaded with the main purpose, and includes a concrete example. Every phrase adds value: the behavior on invalid slugs, the source of the slug, and what the returned details include. No fluff or redundancy.

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?

The description is complete for a tool with a single parameter, high schema coverage, and an output schema present. It covers input derivation, usage context, return value, and edge-case behavior. The output schema likely documents return structure, so not describing it is acceptable. Complexity is low, and no additional guidance seems necessary.

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 parameter is fully documented. The description adds important semantic context by explaining how to derive the slug from toolUrl (e.g., '/tools/acme-writer' -> 'acme-writer'), which is not in the schema. This elevates the score above baseline, though the schema already covers the parameter.

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 tool fetches full public details for one AI tool by slug, specifying the source (listing slug from search_tools) and what is returned (description, launch date, revenue signals, for-sale status). It also mentions returning null for invalid slugs, which distinguishes it from sibling tools like search_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 explicitly instructs to call this after search_tools, providing clear sequential usage. It also differentiates from search_tools by explaining that this retrieves full details for a single tool, while implying search_tools is for listing. No alternative tools are mentioned, but the context of sibling tools (list_alternatives, list_offers) suggests this is for basic tool detail retrieval.

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 convey read-only and idempotent behavior, so the description adds useful context such as 'live' (real-time data) and 'same-category' (filtering criteria). It also warns that target is a well-known tool slug, not a listing slug, which helps prevent misuse. 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?

The description is two sentences, front-loaded with the core purpose and immediately followed by usage guidance and parameter clarification. Every sentence earns its place; no redundancy or 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 simple tool with one parameter, an output schema, and annotations covering safety, the description provides all necessary context: purpose, usage trigger, and parameter clarification. It is complete and self-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?

The schema already documents the target parameter well, but the description adds significant meaning by explicitly stating it is the well-known tool's slug and not a listing slug from this catalog. This distinction is critical for correct usage and goes beyond the schema 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 clearly states the tool lists live, same-category alternatives to a well-known AI tool, with specific examples. It uses a specific verb and resource, and the clarification about target being a well-known tool's slug distinguishes it from sibling tools like search_tools or get_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?

It explicitly states when to call this tool ('when a user asks what are the alternatives to X or wants competitors/substitutes') and clarifies that target is not a listing slug from this catalog. However, it does not explicitly name alternative tools or provide when-not-to-use guidance, so it stops short of a full 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 and idempotentHint. The description adds useful behavioral context beyond annotations: the sponsored label, the free-listing clarification, and the early-bird purchasable=false state. No contradiction with annotations is present.

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 deliver purpose, usage timing, and an important caveat without redundancy. Every sentence adds value and the description is front-loaded with the core action and resource.

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 tool with no parameters and an output schema, the description fully covers what the agent needs: what is returned, how to use it before create_checkout, and the early-access state change. No important operational details are 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?

The tool has zero parameters, so the description bears no parameter-documentation burden. The baseline for 0-parameter tools is 4, and the description appropriately focuses on output semantics rather than inputs.

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 tool 'List the optional paid add-ons a maker can buy to promote their listing' with explicit returned fields (name, effect, price). It distinguishes itself from create_checkout by positioning this as the pre-checkout discovery call, and its resource scope 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?

The description gives an explicit when-to-use instruction: 'Call this to see what's purchasable before create_checkout.' It also explains early-access behavior. However, it doesn't explicitly contrast with sibling list/search tools or state when not to use it, so it falls just 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 indicate readOnlyHint=true, idempotentHint=true, and openWorldHint=false. The description adds useful behavioral detail beyond those: it searches only live/published tools, matching is case-insensitive substring on three fields, and it returns up to 20 results. This clarifies scope and result limits that annotations alone do not convey. 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?

The description is three sentences, each earning its place: what it searches, when to call it first, and result limit. It is front-loaded with the core action and immediately follows with search scope, then usage guidance. No filler or redundant restatement 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?

For a single-parameter, read-only, idempotent search tool with an output schema, the description is highly complete. It covers the data domain, matching behavior, use-case triggers, and result cap. It doesn't explain pagination or exact output structure, but the output schema exists and the operation is intrinsically list-like, so this 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 description coverage is 100%: the 'query' parameter already has a clear description ('Free-text search term, e.g. a tool name, use-case, or category.'). The tool description reiterates the same idea without adding materially new information about parameter format, special syntax, or edge cases, so the 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 names a specific verb ('Search') and resource ('The AI Tools Index catalog of live, published AI tools'), states match fields (tool name, tagline, category name), and specifies the search is free-text and case-insensitive. It also distinguishes from siblings by positioning this as the catalog-wide search entry point, while siblings like get_tool, list_alternatives, and list_offers serve more targeted lookups.

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 explicitly instructs 'Call this first when a user asks to find, discover, or compare AI tools by keyword, use-case, or category', giving concrete triggers and examples. It also implies that this is the initial broad-search tool before narrowing down with siblings, even if it doesn't explicitly enumerate exclusions for each sibling.

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?

Beyond annotations (which are all false), the description discloses key behaviors: the submission is a draft that becomes public only after the maker visits a claimUrl, and the tool is rate-limited globally to 5 submissions per hour. This adds crucial context about side effects and constraints that annotations do not convey.

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 two sentences and front-loaded with the primary action. It packs essential information—purpose, draft workflow, usage restrictions, rate limit, and param guidance—without wasted words. Every sentence 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?

Given the tool's complexity (11 params, 5 required, output schema present), the description covers the submission process, the claim flow, rate limiting, when to call, and which parameters to prioritize. It mentions the key output (claimUrl) and doesn't need to repeat schema details, making it fully adequate for an AI agent to operate 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?

The input schema already describes all 11 parameters (100% coverage), so the description needs to add value beyond that. It does so by advising 'Supply tagline, problemSolved, logoUrl and imageUrls whenever you can' and explaining their impact on listing quality. However, it doesn't add syntax or format details beyond the schema, so a 4 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 uses a specific verb ('Submit a new AI tool to The AI Tools Index on behalf of its maker') and clearly states the resource and outcome ('Creates a draft listing (not yet public) and returns a claimUrl'). This distinguishes it from sibling tools like search_tools or create_checkout, which serve different actions.

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 provides explicit when-to-use ('Call this only when a user explicitly wants to list their own tool') and when-not-to-use ('never to submit a tool on someone else's behalf without their email') guidance. It also notes a global rate limit, preventing misuse. While it doesn't name alternatives, the sibling tools are sufficiently distinct that no alternative is needed.

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