Search the AI Tool Directory catalog: tool details, status checks (alive/acquired/deceased + cause and date), alternatives, and side-by-side comparisons. Read-only.
Scored across 6 tools
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 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?
Adds details on returned fields (status, date, cause, alternatives) beyond annotations. 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?
Three sentences, no fluff. Action, output, and usage guidance are all 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?
Complete for a simple read-only tool with full annotations and single parameter. 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 example. Description does not add extra meaning beyond what schema provides.
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?
Verb 'check' and resource 'AI tool status' are clear. Distinguishes from siblings like get_tool (details) or list_tools (list all).
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?
Explicitly recommends use before recommending a tool. Does not mention when not to use, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare 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 read-only operation. Description adds value by detailing the returned profile (pricing, rating, editorial verdict, lifecycle) and the optional head-to-head verdict and bottom line, going beyond what annotations provide.
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 efficient sentences: first gives the main action, second specifies what is returned. No extraneous information, perfectly 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, so the description fully explains what is returned (profile fields, verdict, bottom line). For a 2-parameter comparison tool with no output schema, this is 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?
Schema coverage is 100% with both parameters documented. The description only reiterates 'by their directory slugs' without adding new meaning beyond the schema's descriptions.
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 'Compare two AI tools side by side by their directory slugs.' with a specific verb and resource, distinguishing it from siblings like list_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?
Implied usage is clear: use when comparing two specific tools. The output details provide context, but no explicit when-not-to-use or alternative references are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find 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, idempotentHint, destructiveHint. Description adds valuable context: returns curated alternatives and special handling for shut-down tools. No 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 concise sentences that front-load the core purpose. No redundant information; every word adds 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 simple lookup tool with one parameter and no output schema, the description covers purpose, behavior, and usage context. Annotations fill safety profile.
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% for the single parameter. Description rephrases 'slug' as 'directory slug', adding minimal extra meaning. 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?
Description clearly states the tool finds curated alternatives by directory slug, with specific behavior for shut-down tools. Distinguishes from sibling tools like check_tool_status or list_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?
Includes a usage example ('what should I use instead of X'), providing clear context. Does not explicitly state when not to use or name alternatives, but the example is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get 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=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing what fields are returned (editorial verdict, rating, last verified date, lifecycle status, site URL), which helps an agent understand the behavioral outcome beyond the 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 a single, well-structured sentence that front-loads the action and tool specificity. Every word adds value with no 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?
Given the tool's simplicity (one required param, read-only, no output schema), the description fully covers what the tool does and what it returns. No gaps remain for an agent to select or invoke it 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% with a clear parameter description for 'slug' including an example. The description only mentions 'by its directory slug' without adding new meaning. Thus the description adds little beyond the schema, justifying a 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?
The description clearly states 'Get the full profile of one AI tool by its directory slug' and enumerates specific fields returned (description, pricing, key features, etc.). It explicitly distinguishes from siblings like compare_tools, find_alternatives, and list_tools by focusing on a single tool's full details.
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 it or provide exclusion criteria. However, the purpose is clear enough that an agent can infer appropriate contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List 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 readOnly, openWorld, idempotent, and non-destructive hints. Description adds 'top-rated active' and optional pricing filtering, which are useful behavioral nuances. 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?
Two sentences, front-loaded with key action, no fluff. Every word 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?
Covers core functionality but lacks details on what 'top-rated' means (e.g., rating threshold, sorting order) and does not mention pagination or result format. Without output schema, description should provide more context on return value structure.
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 covers 100% of parameters with descriptions. Description adds context for role and category via example queries, but does not add significant meaning beyond schema's parameter descriptions.
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?
Clearly states it lists top-rated active AI tools in a category or role, with optional pricing filter. Provides example queries that illustrate usage, but does not explicitly differentiate from sibling 'search_tools' which might offer broader search.
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?
Includes example queries like 'what are the best AI tools for sales' which imply when to use, but no explicit guidance on when not to use or comparison to siblings. Minimal context for alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search 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, openWorldHint, etc. Description adds return structure (slug, description, pricing, rating) and mentions hybrid semantic search, providing behavioral 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 efficient sentences: first explains purpose and output, second gives usage direction. 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 2 simple parameters and no output schema, the description adequately covers purpose, return fields, and sibling relationship. Lacks details on error handling or pagination but is sufficient for basic use.
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 parameters are documented. Description adds minimal extra meaning (hybrid semantic search) but does not elaborate on parameters beyond 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 tool searches an AI Tool Directory catalog by keyword, use case, or category using hybrid semantic search, and specifies the return fields. It also distinguishes from sibling tools by directing to get_tool for full detail.
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 says to use this for discovery then get_tool for full detail, providing when-to-use guidance. It does not explicitly exclude all siblings but implies the flow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
check_tool_statuscompare_toolsfind_alternativesget_toollist_toolssearch_tools
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