Search the AI Tool Directory catalog: tool details, status checks (alive/acquired/deceased + cause and date), alternatives, and side-by-side comparisons. Read-only.
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Average 4.2/5 across 6 of 6 tools scored.
Each tool has a distinct purpose: checking lifecycle status, comparing two tools, finding alternatives, getting full details, listing by category, and searching. No overlap.
All tool names use a consistent verb_noun pattern in snake_case (e.g., check_tool_status, search_tools). Minor singular/plural variation is acceptable.
With 6 tools covering search, browse, detail, comparison, and lifecycle checks, the count is well-scoped for an AI tool directory service.
The tool set covers the main user needs: discovery (search, list), details (get_tool), verification (check_tool_status), comparison (compare_tools), and alternatives (find_alternatives). No obvious gaps.
check_tool_statusCheck if a tool is still activeACheck whether an AI tool is still alive. Returns active, deceased, or acquired — with the date and cause if it shut down, and live alternatives if it did. Use this before recommending a tool to avoid suggesting one that no longer exists.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | Tool name or directory slug to check, e.g. "Jasper" or "jasper-ai". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, etc. The description adds beyond: it discloses that the tool returns status, date, cause, and live alternatives. This is useful behavioral 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?
Two concise sentences. First sentence states function and output. Second provides usage context. No wasted words, 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?
Given the tool's simplicity (one param, no output schema, good annotations), the description covers purpose, output, and usage completely. No 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?
Schema coverage is 100%, so baseline is 3. The description does not add extra parameter details beyond what schema provides, but the single parameter is self-explanatory. No deduction needed.
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 'Check' and clarifies the resource 'tool is still alive'. It details the return states (active, deceased, acquired) and purpose, distinguishing it from sibling tools like list_tools or 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 states when to use ('before recommending a tool to avoid suggesting one that no longer exists') but does not explicitly name alternatives or when-not-to-use. However, the use case is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_toolsCompare two toolsACompare two AI tools side by side by their directory slugs. Returns each tool’s profile (pricing, rating, editorial verdict, lifecycle) plus the editor’s head-to-head verdict and bottom line when one exists for the pair.
| Name | Required | Description | Default |
|---|---|---|---|
| slugA | Yes | Directory slug of the first tool. | |
| slugB | Yes | Directory slug of the second tool. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, idempotent behavior. The description adds that it returns pricing, rating, editorial verdict, lifecycle, and head-to-head verdict, providing useful context beyond 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?
Two concise sentences, front-loaded with action and scope, no redundant words.
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 adequately explains return values (profile fields and verdict). Missing error handling for missing slugs, but overall sufficient given tool simplicity.
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% with descriptive parameter names, so the description adds limited value beyond repeating 'directory slug'. 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 tool compares two AI tools by directory slugs and returns profiles plus editor verdict, which distinguishes it from siblings like list_tools or 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?
It specifies the input as directory slugs and mentions when a head-to-head verdict exists, but could be more explicit about when to use this vs find_alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_alternativesFind tool alternativesAFind curated alternatives to a given AI tool by its directory slug. If the tool has shut down, returns live replacements. Good for "what should I use instead of X" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The directory slug of the tool to find alternatives for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, so the safety profile is clear. The description adds the specific behavior about returning live replacements for shut-down tools, which provides some additional context but does not elaborate on other behavioral aspects like auth or rate limits.
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 with no filler. The first sentence immediately states the primary function, and the second provides a key behavioral note. Every word adds value, making it highly efficient.
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 and no output schema, the description covers the essential purpose and a special case. It omits details about the output format (e.g., returns tool names, slugs, or objects), but given the low complexity, this is not a critical gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the slug parameter described as 'The directory slug of the tool to find alternatives for.' The tool description reinforces 'by its directory slug' but adds no new information beyond the schema. With full coverage, a baseline of 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 tool finds curated alternatives to a given AI tool by its directory slug, and distinguishes from siblings by specifying that it returns live replacements for shut-down tools. The verb 'find' and resource 'alternatives' are unambiguous, and the example usage makes its purpose evident.
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 a clear use case ('what should I use instead of X' questions) and mentions special handling for shut-down tools. It lacks explicit when-not-to-use guidance or direct comparison to siblings, but the context is sufficient for an agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_toolGet tool detailsAGet the full profile of one AI tool by its directory slug: description, pricing, key features, editorial verdict and rating, the date it was last human-verified, lifecycle status, and the official site URL.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The directory slug, e.g. "gamma-app-ai-powered-presenting-ideas" (from search_tools). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by detailing the return fields, but does not introduce any new behavioral traits beyond what annotations imply.
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?
Single well-structured sentence, front-loaded with action, lists what is included with no wasted words.
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 one input param and no output schema, the description fully explains what the tool returns, leaving no 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?
Schema coverage is 100% with a good param description. The description adds context by specifying that the slug comes from search_tools and gives an example, enhancing meaning 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 clearly states the verb 'get' and the resource 'full profile of one AI tool', and lists specific fields (description, pricing, key features, etc.), distinguishing it from siblings 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 implies usage when you have a slug from search_tools, but does not explicitly state when not to use or compare to alternatives like list_tools or check_tool_status.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_toolsList tools by category or roleAList the top-rated active AI tools in a category or for a job role, optionally filtered by pricing. Good for "what are the best AI tools for sales" or "free tools in ".
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Job-role slug, e.g. "sales", "marketing", "customer-support". | |
| limit | No | Max results (1-20, default 8). | |
| pricing | No | Optional pricing filter: Free, Freemium, Free Trial, or Paid. | |
| category | No | Category slug (from search_tools results), e.g. "productivity". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds valuable behavioral context: tools are 'top-rated' and 'active', and results can be filtered by pricing. No contradictions.
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?
Single, well-structured sentence that front-loads the action and resource. Every word provides value, no fluff.
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 list tool with 4 parameters and no output schema, the description explains the filtering (category, role, pricing) and the nature of results (top-rated, active). Lacks clarification on sort order beyond 'top-rated', but overall adequate.
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 baseline is 3. The description does not add significant meaning beyond what the schema already provides for each 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 verb 'List', the resource 'top-rated active AI tools', and the scope 'in a category or for a job role'. It distinguishes itself from sibling tools like 'search_tools' by focusing on top-rated and active filters.
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?
Provides example queries ('best AI tools for sales', 'free tools in <category>') that imply use cases, but does not explicitly state when not to use this tool or contrast it with alternatives like 'search_tools'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_toolsSearch AI toolsASearch the AI Tool Directory catalog (2,000+ AI tools) by keyword, use case, or category using hybrid semantic search. Returns ranked tools with slug, one-line description, pricing model, and rating. Use this to discover tools, then get_tool for full detail.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (1-20, default 8). | |
| query | Yes | What the user is looking for, e.g. "AI video editing" or "alternatives to Jasper". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint. Description adds context on search method (hybrid semantic search) and result fields, which is consistent and adds value without contradiction.
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: first states functionality, second suggests workflow. No fluff, front-loaded with key 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?
Given 2 parameters, no output schema, and comprehensive annotations, the description covers the tool's return values and end-to-end usage. Could mention pagination or limit behavior but not required.
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% with each parameter described. Description adds semantic context by mentioning 'keyword, use case, or category' for the query and 'hybrid semantic search' but does not add significant new meaning 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?
Description clearly states verb 'Search', resource 'AI Tool Directory catalog', and scope 'by keyword, use case, or category' using hybrid semantic search. It mentions return fields (slug, description, pricing, rating) and distinguishes from sibling tool get_tool by suggesting subsequent use.
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?
Provides explicit workflow: 'Use this to discover tools, then get_tool for full detail.' Does not explicitly exclude alternative tools like list_tools, but the workflow guidance is clear enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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