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.
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'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It mentions pagination and scoping to a category, which are useful. However, it does not explicitly state that it is a read-only operation, nor does it describe error behavior or return format. For a simple read tool, this is adequate but not rich.
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 primary function and immediately followed by usage guidance. 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?
Given the simplicity of the tool (list with pagination) and the comprehensive parameter schema, the description covers the essential context. Since there is no output schema, a brief note on return structure would enhance completeness, but the current information suffices for a tool of this complexity.
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 having a description. The description itself adds no additional parameter semantics beyond what the schema already provides, so the baseline of 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?
The description clearly states the tool's function: listing applications within a specific category, with pagination. It explicitly distinguishes itself from searching or getting a specific tool, making it distinct from siblings like search_tools and 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 explicit guidance on when to use this tool: when exploring an area rather than searching for a specific tool. This directly contrasts with search_tools and clarifies the intended use case.
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. Returns structured attributes for both (pricing, deployment, modality, content policy) so the client can recommend which fits a user's needs.
| Name | Required | Description | Default |
|---|---|---|---|
| linkName_a | Yes | The canonical linkName of the first application (slug used in /application/<linkName>). | |
| linkName_b | Yes | The canonical linkName of the second application. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the tool returns structured attributes, implying a read-only operation, and enumerates the attribute categories. However, it does not explicitly disclose whether it modifies data, requires auth, or handles missing tools. This is adequate but not exhaustive.
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 consists of two clear, concise sentences with no redundant wording. It front-loads the core function and then explains the returned data and purpose, earning a perfect score.
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 two parameters and no output schema, the description is fairly complete: it explains the result (structured attributes) and lists the attribute categories. It does not mention error behavior or return format details, but these are not critical for a comparison 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% with both parameters (linkName_a, linkName_b) well-defined. The description adds no additional parameter context beyond the schema, so the baseline score of 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?
The description uses a specific verb ('Compare') and resource ('two AI tools side-by-side'), and lists the returned attributes (pricing, deployment, modality, content policy). This clearly distinguishes it from sibling tools like get_tool (single tool) or get_alternatives (suggesting alternatives, not a direct comparison).
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 for when to use the tool: when a client needs to recommend which of two tools fits a user's needs. It does not explicitly name alternatives or state when not to use it, but the comparison use case is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the tool returns same-category alternatives excluding the original, which is the core behavior. The description implies read-only operation, which is accurate for a lookup tool. It does not mention any side effects or prerequisites, but given the simple read-only nature, the provided behavioral disclosure is strong.
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 concise sentences, front-loaded with the core purpose, followed by usage examples. Every sentence earns its place with no wasted words, achieving excellent conciseness and structure.
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 tool is simple (2 params, no output schema, no annotations), and the description covers the core purpose and usage. However, it omits the limit parameter from the textual description, which is a key aspect of controlling the output. The lack of any return format detail is acceptable given the simple nature, but the limit gap makes it not fully 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 is described, while limit has no description. The description does not compensate for the missing limit explanation—it never mentions how many alternatives are returned or that limit controls the count. Thus it adds no value for the limit parameter, which is a significant gap.
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's function: given a specific AI tool, return similar tools in the same category, excluding the original. This distinguishes it from siblings like search_tools (general search), get_tool (retrieve one), compare_tools (compare), and list_categories (list categories), showing a specific verb+resource scope.
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 it for 'what's like X?' or 'cheaper alternative to Y' questions, providing clear when-to-use guidance. However, it does not mention when not to use or name alternatives, so it falls short of the full exclusions, but the clear context earns a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the full detail page for a specific AI tool by its linkName. Returns name, full description, category, screenshot, and additional information if available.
| Name | Required | Description | Default |
|---|---|---|---|
| linkName | Yes | The canonical linkName of the application (the slug used in /application/<linkName>). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses return contents (name, full description, category, screenshot, additional info) and uses 'Fetch' to imply a read-only operation. It lacks edge-case details like not-found behavior, but this is acceptable 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?
A single sentence clearly states purpose, input, and output with no filler. It is front-loaded and every clause 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 one-parameter read tool with no output schema, the description is sufficient: it states the input, the action, and the returned fields. It could mention error handling, but the low complexity and strong schema coverage make the description complete enough for selection and 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 input schema already fully documents 'linkName' with 100% coverage, including its canonical slug format. The description adds no new semantic detail beyond restating that lookup is by linkName.
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 'Fetch' and clearly identifies the resource: the full detail page for a specific AI tool by its linkName. This distinguishes it from sibling tools like search_tools and browse_category, which handle lists or categories.
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 clearly implies the use case—retrieve details for one known tool by linkName—and contrasts with broader browsing/searching tools. However, it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only restates the core function. It does not describe output format, ordering, how category filtering behaves, or error handling. The 'curated editorial' phrasing adds minimal 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?
The description is two sentences, front-loaded with the action, and contains no fluff or repetition. Every phrase 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 tool with two optional parameters and no output schema, the description conveys the core purpose and use cases. However, it lacks details about the return structure or what constitutes a 'pick', which would be useful given no 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?
Schema covers 50% of parameters; 'category' is described in the schema, and 'limit' has clear constraints (default, min, max). The description adds usage context ('top X in Y') but does not further explain parameter semantics beyond what the 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?
The description clearly states the tool returns 'curated editorial picks' with an optional category scope, which is a specific verb+resource+scope. It distinguishes itself from siblings like search_tools or browse_category by emphasizing the 'editorial' nature of the results.
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 usage scenarios: 'recommend the best AI tools' or 'top X in Y' questions. This clearly signals when to use the tool, though it doesn't explicitly mention alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all categories in the directory. Useful when the user wants to browse by topic or narrow a search by category.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'List all categories in the directory,' which implies a read-only operation and clarifies the scope (all categories, in the directory). While it does not explicitly mention side effects or permissions, the action 'list' is inherently non-mutating, and the added scope goes beyond the tool name.
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, both earning their place. The first sentence states the action and scope; the second provides usage context. It is front-loaded with the core function and contains no redundant or extraneous 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?
Given the tool's low complexity (zero parameters, no output schema, no annotations), the description is complete. It fully explains what the tool does and when to use it, leaving no critical gaps. No additional return-format or prerequisite details are necessary for a simple list operation.
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 schema description coverage is trivially 100%. Per the rubric, 0 parameters warrants a baseline score of 4. The description does not add parameter-specific details because there are none, which 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's function: 'List all categories in the directory.' The verb 'List' is specific and the resource 'categories' is well-defined. The word 'all' distinguishes it from sibling tools like browse_category or search_tools, making the purpose 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 provides clear usage context: 'Useful when the user wants to browse by topic or narrow a search by category.' This tells the agent when to invoke this tool, though it does not explicitly name alternatives or state when not to use it. This falls short of a 5 but is clear enough for a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns a ranked list with name, URL, and short description, and that it matches a query. This is sufficient to understand the read-only behavior and output, though it does not detail ranking semantics or edge cases.
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 action and immediately states the result format and primary use case. Every sentence earns its place, 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?
The tool has 3 params (1 required), no output schema, and no annotations. The description covers the core purpose, output fields, and a concrete usage scenario. While it doesn't mention category filtering or ranking order, the schema fills some gaps, and the overall description is reasonably complete for a search 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 coverage is 100%, so the baseline is 3. The description adds minor context by framing the query as 'find me a tool that does X' and mentioning output fields, but it does not add meaning to the category or limit parameters beyond what the schema already states. The description does not improve parameter understanding significantly.
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 the AI Collection directory and returns a ranked list with name, URL, and short description. It uses a specific verb ('Search'), resource ('AI Collection directory'), and result format, distinguishing it from sibling tools like 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 explicitly provides a use case: 'Use this for "find me a tool that does X" requests.' This gives solid context for when to use the tool. It does not mention when not to use alternatives, but the context is clear enough that a 4 is appropriate rather than a 5.
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. Dates show when Glama detected each change.
compare_tools1 field changedInput schema / properties / include_nsfwRemoved value: -{
- "default": false,
- "description": "If false (default), the tool refuses when either app is in an NSFW category. Set true to include NSFW apps explicitly.",
- "type": "boolean"
-}list_categories2 fields changedInput schema / additionalPropertiesRemoved value: -falseInput schema / properties / includeNSFWRemoved value: -{
- "default": false,
- "description": "If true, include NSFW categories.",
- "type": "boolean"
-}search_tools1 field changedInput schema / properties / includeNSFWRemoved value: -{
- "default": false,
- "description": "If true, include results from the NSFW category.",
- "type": "boolean"
-}compare_toolslist_categories1 field changedInput schema / properties / includeNSFW / descriptionPrevious value: -"If true, include the NSFW category."New value: +"If true, include NSFW categories."browse_categoryget_alternativesget_toolget_top_pickslist_categoriessearch_toolsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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Each tool has a clearly distinct purpose: browsing a category, comparing two tools, finding alternatives, fetching tool details, getting top picks, listing categories, and searching. There is no ambiguity or overlap.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., browse_category, compare_tools, get_tool). The naming is predictable and uniform across the set.
With 7 tools, the set is well-scoped for an AI tool directory. Each tool serves a specific and necessary function, and there is no unnecessary bloat or deficiency.
The tool surface covers all core use cases for a directory: browsing by category, searching, getting details, comparing, and finding alternatives. Minor gaps like missing 'top trending' or user-specific features are acceptable and do not hinder typical workflows.