Launch board for MCP products: discover probed listings, search by real tools, submit as a maker.
Find live FastDrop products by capability. Searches the actual tool lists FastDrop read from each endpoint via tools/list - not maker-supplied tags or descriptions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results, 1-50, default 20 | |
| query | Yes | Capability, tool name, or description to match |
| Name | Required | Description |
|---|---|---|
| products | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark as readOnly and idempotent. Description adds that it searches actual tool lists from endpoints, not maker-supplied tags, which clarifies behavior and data source 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 with purpose front-loaded. No filler words, but could clarify product/tool ambiguity. Efficient for a simple tool.
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 output schema exists and annotations provide safety hints, description adequately explains purpose and data source. Does not need to describe return values. Slight gap in clarifying scope of search.
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 description need not add detail. It mentions 'by capability' but does not describe the parameters beyond what schema provides. Baseline score 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?
States finding live FastDrop products by capability, and clarifies it searches actual tool lists, not tags. The verb 'find' and resource 'products' are clear, but the tool name is 'find_tools' which creates minor confusion. Still differentiates from siblings by specifying data source.
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?
Indirectly suggests use when you need real tool capabilities from endpoints, not tags. Does not explicitly state when to use or provide alternative tool names, but context from sibling tools and description helps. Lacks clear exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get one live FastDrop product by its slug. Returns the MCP URL if set, mcpVerified, last-verified timestamp when available, and the probed tool list (name + description) read from the endpoint - not maker-supplied tags.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug from the /p/<slug> URL |
| Name | Required | Description |
|---|---|---|
| product | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral details: it returns MCP URL, mcpVerified, last-verified timestamp (when available), and probed tool list read from endpoint (not maker-supplied tags). This enriches the agent's understanding beyond the structured 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, clear sentence that efficiently conveys the tool's action and return data. It is front-loaded with the core purpose. No unnecessary words. However, it could be slightly more structured, e.g., separated into purpose and return information, but it is still concise.
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 parameter, read-only, has output schema), the description is fully sufficient. It covers what the tool does, how to identify the resource (slug), and what data to expect. No omissions for this level of 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 the slug parameter well-described as 'Product slug from the /p/<slug> URL'. The description does not add new details about the parameter beyond what the schema provides. It mentions the return fields indirectly, but that does not enhance parameter understanding. Baseline score 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 identifies the verb 'Get', the resource 'one live FastDrop product', and the identifier 'by its slug'. It distinguishes the tool from siblings like search_products and list_comments by specifying a single product retrieval. The return fields are also listed, leaving no ambiguity about the tool's purpose.
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 when to use this tool: when you have a slug and need product details. It does not explicitly state when not to use it or suggest alternatives, but the sibling tool names provide context for differentiation. A more explicit exclusion statement would warrant a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List comments for a live FastDrop product by its slug, as a two-level tree (top-level comments and their replies). Removed comments appear as a {deleted: true} tombstone. Unauthenticated - available to any agent.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug | |
| limit | No | Max top-level results, 1-50, default 20 |
| Name | Required | Description |
|---|---|---|
| comments | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds beyond this: it reveals the two-level tree structure and that removed comments appear as tombstones, which is useful for agent understanding.
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 concise: two sentences that are front-loaded with the core purpose and immediately provide valuable detail. 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?
Given the rich annotations, output schema, and clear parameter schema, the description adds necessary context (tree structure, tombstone, authentication) to make the tool fully self-explanatory.
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 has 100% description coverage for both parameters, so the description adds no additional meaning. This meets the baseline expectation.
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 it lists comments by product slug, and specifies the two-level tree structure (top-level and replies). This distinguishes it from sibling tools like list_reviews or get_product.
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: it lists comments for a live FastDrop product by slug, and notes it is unauthenticated. However, it does not explicitly state when not to use it or compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List live product launches on FastDrop, ranked by votes. Each product may include an MCP endpoint URL, mcpVerified (whether FastDrop probe succeeded), and mcpAuthRequired when the endpoint answered 401/403.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results, 1-50, default 20 | |
| window | No | Rank by votes cast in this window (UTC). Default all-time. |
| Name | Required | Description |
|---|---|---|
| products | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by detailing specific fields returned (MCP endpoint URL, mcpVerified, mcpAuthRequired), which is beyond basic safety traits.
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 the action, no redundant information. Every sentence contributes meaningful detail.
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 ranking and special fields. Output schema exists to explain return format. Could mention pagination or error handling but not required given output schema. 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 coverage is 100% with both parameters (limit, window) described in the schema. The description does not add additional meaning or context beyond what the schema provides, so 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 action (List), resource (live product launches), and context (ranked by votes). It differentiates from sibling tools like list_comments or list_reviews by specifying launches.
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 for viewing ranked launches but does not explicitly state when to use this tool vs alternatives like search_products or find_tools. No when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List reviews for a live FastDrop product by its slug. Returns the average rating, total count, and individual reviews (rating, body, author, date). Unauthenticated - available to any agent.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug | |
| limit | No | Max results, 1-50, default 20 |
| Name | Required | Description |
|---|---|---|
| slug | Yes | |
| count | Yes | |
| average | Yes | |
| reviews | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description adds that it is unauthenticated and details return data, going 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?
Two concise sentences with front-loaded purpose. No unnecessary 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 rich annotations, complete schema, and output schema, the description covers essential aspects: purpose, authentication, and return data. No gaps identified.
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 about 'by its slug' but does not significantly exceed schema explanation. 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 'List reviews for a live FastDrop product by its slug' with specific verb and resource, and includes return fields. Distinguishes from siblings like list_comments and list_launches.
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 mentions 'Unauthenticated - available to any agent' and implies usage for reading reviews. Lacks explicit when-not-to-use but provides sufficient context given siblings are for different resources.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search live FastDrop products by name/tagline text and/or curated tag. Optionally filter to x402-payable services (listed in the Bazaar catalog). Results ranked by votes and include verification status (mcpVerified) and freshness where available.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Curated tag slug | |
| x402 | No | If true, only return products marked x402-payable via Bazaar enrichment | |
| limit | No | Max results, 1-50, default 20 | |
| query | No | Text matched against name and tagline |
| Name | Required | Description |
|---|---|---|
| products | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds behavioral context beyond annotations: results ranked by votes, include verification status and freshness. It also mentions 'live' products and optional x402 filtering. No contradiction with readOnlyHint, idempotentHint, etc.
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 dense sentences, front-loaded with core action, no fluff. Could slightly improve by separating filter and ranking details, but overall 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?
Given an output schema exists, the description covers key output fields (verification, freshness) and ranking. Mentions pagination implicitly via limit. Sufficient for agent to understand tool behavior.
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 3. Description explains query/tag matching and limits, but does not add significant new semantic detail beyond what the schema already provides (e.g., parameter names, 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 the purpose: search live FastDrop products by text, tag, or both, with optional filter and ranking. It distinguishes itself from siblings like get_product (single product) and list_* (list all) by being a search 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?
No guidance on when to use this tool versus siblings like find_tools or list_launches. The description implies it's for searching products, but does not specify when to prefer this over other tools.
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.
find_toolsget_productlist_commentslist_launcheslist_reviewssearch_productsClaiming 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
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Each tool has a clearly distinct purpose: searching by capability, getting product details, listing comments, launches, reviews, and searching by text/tags. No overlap in functionality.
All tool names follow a consistent verb_noun pattern in snake_case (find_tools, get_product, list_comments, list_launches, list_reviews, search_products), with clear and predictable verbs.
Six tools is appropriate for a product discovery server, covering search, detail retrieval, and listing of associated data without being excessive or insufficient.
The tool set covers the core operations for the domain: searching, viewing individual products, and browsing launches, comments, and reviews. No obvious gaps for a read-only discovery service.