Launch board for MCP products: discover probed listings, search by real tools, submit as a maker.
Scored across 8 tools
Most tools target distinct resources: get_maker, get_product, list_comments, list_reviews are clearly scoped. However, find_tools, search_products, list_launches, and get_engagement_feed all surface products with overlapping intent, so an agent may hesitate between capability search, text search, ranked listing, and the feed.
Every tool follows a clean verb_noun pattern (find_tools, get_maker, get_product, list_comments, list_launches, list_reviews, search_products), with only get_engagement_feed being longer but still consistent. No mixed casing or verb-style drift.
Eight tools is well-scoped for a product-discovery and engagement directory. Each tool covers a distinct aspect (search, feed, maker, product, comments, launches, reviews) without redundancy in count.
The surface is read-only, yet get_engagement_feed explicitly references write actions (toggle_vote, post_review, commenting) that have no corresponding create/post tools. Listing comments and reviews exists, but submitting them and voting do not, leaving the stated participation loop unreachable for agents.
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 cover the safety profile (readOnly, idempotent, openWorld, non-destructive), so the bar is lower. The description adds genuinely useful behavior beyond that: results reflect live endpoint data read via tools/list rather than maker-supplied tags or descriptions, which tells the agent something about freshness and reliability. It still omits pagination or limit behavior.
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 short sentences with no filler, and the core purpose is front-loaded ahead of the provenance caveat. Every clause 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?
With an output schema present, return values needn't be explained, and both parameters are fully documented in the schema. What is missing is orientation against the sibling search_products and any note about how limit/paging affect discovery, which keeps it short of a 5.
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 query and limit documented in the schema itself, so baseline 3 applies. The description's 'by capability' hints at matching semantics but adds no format, syntax, or matching-rule detail beyond what the schema already states.
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 a specific verb (Find), resource (live FastDrop products), and matching dimension (capability), and clarifies the data source as the actual tools/list output. It does not, however, distinguish itself from the sibling search_products, which an agent could easily confuse it with.
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?
There is no explicit when-to-use, when-not-to-use, or alternative-tool guidance. The closest thing to context is the provenance note about tools/list, which is about data accuracy rather than selection between this and search_products or get_product.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Live FastDrop launches that need attention right now - fewest votes first, zero-review items flagged. The participation loop: vote on, review, or comment on 3 different launches in the last 7 days and your submission moves to the front of the human review queue. With auth, the feed is personalized (your own launches and already-voted items excluded, tag-matches with your launches ranked first), each item says whether it needs a vote (toggle_vote) or a review (post_review), and the boost object reports your progress. Unauthenticated callers get the generic feed.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results, 1-20, default 5 |
| Name | Required | Description |
|---|---|---|
| boost | No | |
| launches | Yes | |
| personalized | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds substantial behavioral context beyond these: the participation loop (vote/review/comment on 3 launches), the boost object reporting progress, personalization rules (exclusions, tag-ranking), and the per-item indication of needed action (toggle_vote vs post_review). This goes far beyond the annotations and fully discloses behavior without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Though longer than average, every sentence carries distinct information: the feed's ordering and flags, the participation loop's mechanics, auth-specific behavior, per-item action hints, and boost progress. The most critical scoping (live launches needing attention) is front-loaded. No filler or repetition – it is information-dense yet 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 the tool's complexity (personalization, participation loop, boost object) and that an output schema exists, the description covers all necessary call-time context: auth vs unauth behavior, what the feed contains, what each item indicates, and how the boost object works. Nothing an agent needs to decide whether and how to call it is missing.
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 'limit' parameter, which is fully described in the schema ('Max results, 1-20, default 5'). The description does not add anything about limit semantics, which is fine because the schema already covers it. Baseline 3 applies; no additional value is provided or needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: 'Get engagement feed' and immediately defines its scope: 'Live FastDrop launches that need attention right now - fewest votes first, zero-review items flagged.' This clearly distinguishes it from sibling tools like list_launches or list_reviews, which are general listing operations. The participation loop and personalization details further cement its unique 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 gives strong context on when to use it: for live launches needing attention, with a clear distinction between authenticated (personalized feed) and unauthenticated (generic feed) callers. It does not explicitly name alternative tools or state when not to use it, but the purpose is so specialized that the intended usage is unambiguous. Slight deduction for lacking explicit 'use X instead when Y' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a builder's public FastDrop profile by handle (from a product's maker.handle or a /u/ URL): their self-chosen label, bio, links, awards, public stats (products, upvotes, MCP servers online, AI agent lookups), and every live product they launched (50 per call, ranked by votes - page with offset/nextOffset; ranks can shift between calls as votes change). Public data only - no contact details.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | Profile handle, without the @ | |
| offset | No | Skip this many products (ranked by votes) - pass nextOffset from the previous call. Default 0. |
| Name | Required | Description |
|---|---|---|
| maker | Yes | |
| stats | Yes | agentLookups and agentClients are withheld (null / empty) below a minimum count. |
| awards | Yes | |
| products | Yes | Up to 50 products ranked by votes, starting at offset |
| nextOffset | No | Present when more products remain - call again with this offset |
| productsTotal | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover read-only, idempotent, non-destructive, and open-world. The description adds real behavioral context beyond them: page size (50 per call), that results are ranked by votes and ranks can shift between calls, and that only public data is returned with no contact details. Rate limits aren't mentioned, but the ranking volatility and pagination details are valuable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single dense sentence that front-loads the core purpose before drilling into return contents and pagination. Every clause carries information, though the run-on structure is slightly heavy relative to ideal chunking.
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 an output schema present and 100% parameter coverage, the description needn't restate return values, yet it still covers pagination, ranking volatility, and data-scope limits (public only). Nothing an agent needs to call this correctly is missing.
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, but the description adds meaning: it clarifies the handle's origin/format (maker.handle or /u/<handle>) and explains offset's semantics (paging the vote-ranked list via nextOffset). That goes beyond the schema's own text.
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 a specific verb and resource ('Get a builder's public FastDrop profile by handle') and enumerates exactly what is returned (label, bio, links, awards, stats, live products). The resource is clearly distinct from the product/review/comment/tool siblings, so an agent can route to it without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It tells the agent where the required handle comes from ('a product's maker.handle or a /u/<handle> URL'), which is the key prerequisite for calling it. It doesn't explicitly name an alternative sibling or state exclusions, but usage context is clear.
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 readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds real value beyond that by disclosing what is returned and by flagging that the probed tool list is read from the endpoint "not maker-supplied tags" — a provenance distinction an agent could not infer from the schema or 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 short sentences, front-loaded with the action and scope, with no filler. Every clause carries information — the lookup key, the liveness constraint, and the returned payload characteristics.
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 annotations covering safety and an output schema covering the response shape, the description only needs to explain what makes this lookup distinctive, which it does. The one remaining gap is error/not-found behavior for an unknown or delisted slug.
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% and the single slug parameter is documented in the schema as coming from the /p/<slug> URL. The description adds nothing beyond that, 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?
States a specific verb and resource ("Get one live FastDrop product by its slug") and scopes it to a single live product, which implicitly separates it from search_products and list_* siblings. It stops short of naming an alternative explicitly, so an agent must infer that the list/search siblings are for the discovery case.
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?
Usage is implied by the slug requirement and the "live" qualifier, but the description never states when to prefer this over search_products or find_tools, nor what happens for a non-existent or non-live slug. No exclusions or prerequisites are given.
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 readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds payload semantics (MCP endpoint URL, mcpVerified from the probe, mcpAuthRequired on 401/403), which is genuinely useful context, but it says nothing about pagination or how 'live' is determined.
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 tight sentences, front-loaded with the core action and ordering. The second sentence is a compact field enumeration that earns its place, though it could be trimmed given an output schema exists.
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 an output schema present, return values need not be explained, yet the description still clarifies the MCP-related fields, which helps interpretation. What is missing is guidance on scope/filtering relative to sibling list tools, a minor gap for a zero-required-parameter read 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%, so both parameters (limit, window) are already documented, including the enum and default. The description only indirectly hints at 'window' via 'ranked by votes' and adds no format or range detail beyond the schema, so the baseline 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?
States a specific verb and resource ('List live product launches on FastDrop') plus the ordering rule ('ranked by votes'). This is distinguishable from siblings like search_products or get_product, though it never names an alternative to route between them.
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?
There is no when-to-use / when-not-to-use guidance and no mention of the sibling tools that overlap (search_products, find_tools). The listing intent is self-evident from the name, but the description leaves all routing to inference.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds ranking (by votes) and the presence of mcpVerified/freshness metadata, which is extra behavioral context, though much of it is return-value detail better placed in the output schema.
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 tight sentences, front-loaded with the core searchable fields before the optional x402 filter and result metadata. No filler.
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?
An output schema exists, so explaining return fields is not strictly required, yet the description remains sufficiently complete for a zero-required-param search tool. It could be tighter by omitting return-value detail, but nothing an agent needs to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all four parameters are already documented in the schema. The description adds only marginal meaning (that text matches name and tagline, and that x402 comes via Bazaar enrichment) beyond what the schema states.
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 a specific verb (Search) and resource (live FastDrop products) with the matchable fields (name/tagline text, curated tag) and the optional x402 filter. An agent can distinguish this query-style tool from the singular get_product lookup without opening either schema.
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 (text search over products, optional payment filtering) but never states when to choose this over siblings like find_tools or get_product, nor any exclusions. Usage is inferable from context rather than explicit.
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.
get_maker1 field changedOutput schema / properties / maker / properties / hasSimplexAddressAdded value: +{
+ "description": "The maker accepts SimpleX messages. The address is shown only to signed-in humans on the profile page.",
+ "type": "boolean"
+}find_tools1 field changedOutput schema / properties / products / items / properties / makerAdded value: +{
+ "description": "Who built the product. label is their own description of themselves (e.g. \"Indie hacker\"), \"Builder\" when unset. handle and profile are present once they have a public FastDrop profile.",
+ "properties": {
+ "handle": {
+ "type": "string"
+ },
+ "label": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "profile": {
+ "description": "Public profile URL",
+ "type": "string"
+ }
+ },
+ "required": [
+ "name",
+ "label"
+ ],
+ "type": "object"
+}get_makerget_product1 field changedOutput schema / properties / product / properties / makerAdded value: +{
+ "description": "Who built the product. label is their own description of themselves (e.g. \"Indie hacker\"), \"Builder\" when unset. handle and profile are present once they have a public FastDrop profile.",
+ "properties": {
+ "handle": {
+ "type": "string"
+ },
+ "label": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "profile": {
+ "description": "Public profile URL",
+ "type": "string"
+ }
+ },
+ "required": [
+ "name",
+ "label"
+ ],
+ "type": "object"
+}list_launches1 field changedOutput schema / properties / products / items / properties / makerAdded value: +{
+ "description": "Who built the product. label is their own description of themselves (e.g. \"Indie hacker\"), \"Builder\" when unset. handle and profile are present once they have a public FastDrop profile.",
+ "properties": {
+ "handle": {
+ "type": "string"
+ },
+ "label": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "profile": {
+ "description": "Public profile URL",
+ "type": "string"
+ }
+ },
+ "required": [
+ "name",
+ "label"
+ ],
+ "type": "object"
+}search_products1 field changedOutput schema / properties / products / items / properties / makerAdded value: +{
+ "description": "Who built the product. label is their own description of themselves (e.g. \"Indie hacker\"), \"Builder\" when unset. handle and profile are present once they have a public FastDrop profile.",
+ "properties": {
+ "handle": {
+ "type": "string"
+ },
+ "label": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "profile": {
+ "description": "Public profile URL",
+ "type": "string"
+ }
+ },
+ "required": [
+ "name",
+ "label"
+ ],
+ "type": "object"
+}get_engagement_feedfind_toolsget_productlist_commentslist_launcheslist_reviewssearch_productsMCP registry & directory: search, find & install 31k+ MCP servers & tools. Catalog and marketplace.
Agent-native launch platform and tool directory: search, alternatives, trending, launch via MCP.
Search the official MCP registry: 17,000+ servers with trust grades, stars, tools, install config.
LaunchOnIt is a weekly launch platform and discovery directory for modern SaaS, developer tools, and AI startups. This MCP server connects AI assistants directly to LaunchOnIt’s live software catalog, rankings, and launch infrastructure. Search launched products, weekly leaderboards and podium winners, check open launch slots, and submit a product launch for review.
Add one secure layer between your agents and this server.
