AI Collection
Server Details
Read-only MCP connector for searching and discovering 3,000+ AI tools from AI Collection. Includes tools for search, categories, tool details, alternatives, and curated top picks.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4/5 across 6 of 6 tools scored. Lowest: 3.4/5.
Each tool has a clearly distinct purpose: browse_category lists tools in a category, get_alternatives finds similar tools, get_tool fetches details, get_top_picks gives recommendations, list_categories shows categories, and search_tools finds tools by query. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., browse_category, get_alternatives, list_categories). This makes the set predictable and easy to understand.
With 6 tools, the coverage is well-scoped for an AI directory. Each tool serves a distinct user need (browsing, searching, getting details, recommendations) without being overwhelming or insufficient.
The set covers the core workflow: discovering categories, browsing tools, searching, viewing details, and getting recommendations. A minor gap is the lack of filtering or sorting within search results, but the essential operations are present.
Available Tools
6 toolsbrowse_categoryAInspect
List applications inside a specific category, paginated. Use this when the user wants to explore an area rather than search for a specific tool.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Page size. | |
| offset | No | Number of results to skip for pagination. | |
| linkName | Yes | The category linkName (e.g. 'image-generation'). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It clearly states the tool lists applications, is paginated, and restricts to a category. However, it omits details like result ordering or any side effects, which would elevate it to 5.
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 efficiently conveys purpose and usage guidance without unnecessary 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?
Given the tool's simplicity (no output schema, low parameter count), the description is complete enough. It explains the action, scope, and pagination. A 5 would require some mention of the return format or default behaviors.
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 all three parameters already well-described in the schema. The description does not add additional semantic context beyond the schema, 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 uses a specific verb ('List') and resource ('applications inside a specific category') with pagination context. It clearly distinguishes the tool's exploratory purpose 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?
Explicitly states when to use this tool: 'Use this when the user wants to explore an area rather than search for a specific tool.' This provides clear guidance and implicitly contrasts with search_tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_alternativesAInspect
Given a specific AI tool, return similar tools (same category, excluding the original). Use for 'what's like X?' or 'cheaper alternative to Y' questions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| linkName | Yes | The linkName of the application to find alternatives for. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. States behavior: returns similar tools in same category, excluding original. However, does not disclose how similarity is determined, ordering, or behavior on no results.
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, each serving a purpose: first states function, second provides usage examples. 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?
Description covers core purpose and usage context. Lacks information about output format (e.g., list of tool names) but sufficient for intended queries. With no output schema, could be more 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 50% (only linkName described). Description adds meaning to linkName by linking to 'specific AI tool' and 'cheaper alternative'. limit parameter is not elaborated beyond schema defaults.
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 verb 'return' and resource 'similar tools' with context 'same category, excluding the original'. It also provides example queries, distinguishing it from sibling tools like get_tool 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?
Explicit usage examples ('what's like X?' or 'cheaper alternative to Y') give clear context. Does not mention when not to use, but examples imply appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_toolBInspect
Fetch the full detail page for a specific AI tool by its linkName. Returns name, full description, category, tags, screenshot, and additional information if available.
| Name | Required | Description | Default |
|---|---|---|---|
| linkName | Yes | The canonical linkName of the application (the slug used in /application/<linkName>). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description carries full burden. It details returned fields but discloses no behavioral traits (e.g., side effects, authentication needs, rate limits). Insufficient for a fetch operation.
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 with front-loaded purpose. No wasted words; every sentence 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?
Given low complexity (one parameter, no output schema), description covers return values adequately. Lacks mention of error conditions or edge cases, but not critical for this tool.
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% for the single parameter linkName with clear explanation. Description adds context about returns but does not significantly enhance parameter understanding 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?
Description clearly states verb 'Fetch' and resource 'full detail page for a specific AI tool by its linkName'. Lists returned fields, distinguishing from sibling tools like browse_category and get_alternatives.
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 guidance on when to use this tool vs alternatives. Only implies usage when you have a linkName. Does not mention when not to use or provide comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_top_picksAInspect
Return curated editorial picks across the directory, or within a specific category if provided. Use for 'recommend the best AI tools' or 'top X in Y' questions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| category | No | Optional category linkName to scope the picks. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that picks are curated and can be scoped by category, but lacks details on safety (read-only), data freshness, or return format. Adequate for a simple read 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?
Two sentences, extremely concise. First sentence defines functionality and conditional behavior, second sentence gives usage examples. 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 simple tool with two optional parameters and no output schema, the description covers main functionality and when to use. Lacks mention of return format or limit parameter details, but overall 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 coverage is 50% (category has description, limit does not). Description adds meaning by linking limit to the 'top X' example, but does not explicitly describe limit's default or range. Adds some value beyond schema, but not fully compensating.
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 it returns curated editorial picks, optionally filtered by category, and provides example use cases like 'recommend the best AI tools' or 'top X in Y' questions. This distinguishes it from siblings like browse_category 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?
Explicitly specifies when to use: for 'recommend the best AI tools' or 'top X in Y' questions. Does not explicitly mention when not to use or list alternatives, but the context and sibling names imply differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspect
List all categories in the directory with their tags. Useful when the user wants to browse by topic or narrow a search by category.
| Name | Required | Description | Default |
|---|---|---|---|
| includeNSFW | No | If true, include the NSFW category. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It adds that categories are listed 'with their tags', which is a useful behavioral detail beyond the read-only nature implied by 'list'.
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 only two sentences, front-loaded with the main purpose and followed by usage guidance. Every sentence adds value with no unnecessary 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?
For a simple tool with one optional parameter and no output schema, the description adequately covers what the tool does, its output (categories with tags), and when to use 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%, so baseline is 3. The description adds no additional meaning about the includeNSFW parameter beyond what the schema already provides (boolean, default false).
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 all categories with their tags, providing a specific verb and resource. It distinguishes from siblings like browse_category (which likely focuses on one category) and 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 suggests using the tool when browsing by topic or narrowing a search, offering clear context. However, it does not explicitly state when not to use it or compare with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_toolsAInspect
Search the AI Collection directory for AI tools matching a query. Returns a ranked list with name, URL, and short description. Use this for 'find me a tool that does X' requests.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return. | |
| query | Yes | Free-text query to match against application names and descriptions. | |
| category | No | Optional category linkName to scope the search (e.g. 'image-generation'). | |
| includeNSFW | No | If true, include results from the NSFW category. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool 'Returns a ranked list with name, URL, and short description', which is transparent about output. However, it does not explicitly state that the operation is read-only or disclose any other behavioral traits like idempotency or side effects. A 3 is adequate given the context of a search 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?
The description is extremely concise: two sentences covering functionality, output, and usage context. No filler or redundant information. It is well-structured and immediately actionable.
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?
Without an output schema, the description explains the return format (ranked list with name, URL, short description). However, it lacks details on sorting criteria, pagination, error handling, or any limitations. For a straightforward search tool, it is minimally complete but could be richer.
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?
All four parameters are fully described in the input schema (100% coverage). The tool description adds context about the return format but not parameter-specific semantics beyond what the schema provides. Baseline 3 is appropriate since the schema does the heavy lifting.
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 action ('Search the AI Collection directory') and the resource ('AI tools matching a query'). It distinguishes itself from sibling tools by explicitly recommending use for 'find me a tool that does X' requests, setting it apart from browse_category 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?
The description provides a direct usage guideline: 'Use this for 'find me a tool that does X' requests.' This clearly indicates when to use the tool. However, it does not explicitly mention when not to use it or name alternative siblings for other use cases, which would deserve a 5.
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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{
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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