Alternativas a las herramientas de IA más conocidas, con ingresos verificados con Stripe.
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Average 4.6/5 across 6 of 6 tools scored.
Each tool has a clearly distinct purpose: search, get details, list alternatives, list offers, create checkout, and submit. No two tools overlap in function; even search_tools and list_alternatives serve different intents (free-text query vs. competition lookup). Agents can unambiguously select the right tool for any action.
All tool names follow a consistent verb_noun snake_case pattern: create_, get_, list_, submit_. The verbs are specific and the nouns clarify the target resource. Naming is uniform and predictable across the entire set.
Six tools is within the ideal 3-15 range for a domain-specific server. The scope (search, browse, submit, and purchase add-ons) is well-covered without unnecessary bloat. Each tool earns its place and none feel redundant.
The core workflows—discovering tools, viewing details, finding alternatives, submitting a new listing, and purchasing add-ons—are all covered. Missing are update/delete operations for listings, but those may be handled externally. The surface is complete enough for typical use cases, with only minor gaps.
create_checkoutCreate a checkout link for a paid add-onAInspectCreate a Stripe checkout URL for one paid add-on on one listing, and return it for a HUMAN to open. Never present this as a completed purchase: nothing is charged until a person completes checkout in a browser. Returns an error when the listing doesn't exist, the add-on isn't purchasable yet (free early access), or payments aren't configured in this environment.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The listing's slug (as in a toolUrl, e.g. '/tools/acme-writer' -> 'acme-writer'). | |
| product | Yes | Add-on key: premium_launch, review_one, review_monthly. See list_offers for names, effects, and prices. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| error | No | |
| checkoutUrl | No | A URL a HUMAN must open. Nothing is charged until a person completes it. |
| instruction | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only, non-idempotent, and open-world behavior. The description adds critical behavioral context: the actual charge only happens after a human completes checkout, and it lists specific error scenarios. This goes beyond what annotations convey, making the tool's side effects and limitations clear.
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 three focused sentences: the action, the key caveat, and the error conditions. It is front-loaded and every sentence provides essential information without redundancy or 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?
With only 2 parameters, an output schema, and relevant annotations, the description fully covers the tool's operation. It explains the human-in-the-loop nature, the charging behavior, and failure conditions, making it sufficiently complete for correct usage.
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 slug and product well-described. The description adds minimal semantic value for parameters, only reinforcing that it takes one listing and one add-on. The existing schema descriptions already cover the meaning, 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: 'Create a Stripe checkout URL for one paid add-on on one listing.' It uses a specific verb (create), identifies the resource (checkout URL), and scopes it to a single listing and add-on. This differentiates it from sibling tools like list_offers or submit_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 clear context on how to use the result ('return it for a HUMAN to open') and warns against treating it as a completed purchase. However, it does not explicitly mention alternatives or when to use a different tool, such as list_offers, to check purchasability first. The error conditions give some implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_toolGet AI tool detailAFetch the full public detail for one AI tool by its listing slug (as returned by search_tools' toolUrl, e.g. '/tools/acme-writer' -> slug 'acme-writer'). Call this after search_tools to get a tool's full description, launch date, revenue signals (verified or self-reported), and for-sale status. Returns null if the slug doesn't resolve to a live Alternativas IA listing.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The tool's listing slug. |
| Name | Required | Description |
|---|---|---|
| tool | Yes | null when the slug does not resolve to a live listing on this site. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description wisely adds value by scoping what 'public detail' means (verified or self-reported revenue signals, for-sale status) and the null-return contract. It doesn't add auth, rate-limit, or ordering details, but for a simple read operation with strong annotation coverage, it discloses more than the calibration baseline (which scored 3).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three tightly packed sentences, each earning its place: what it does, when to call it, and the null edge case. The inline example is efficient, and there is zero fluff or repetition of schema data. This is a model of dense, front-loaded technical writing.
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 single-parameter, read-only tool with an output schema, the description covers the input contract (slug format, source), the output scope (public detail, revenue signals, for-sale status), and the failure mode (null case). No critical context is missing for an agent to use this safely and correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100%, so the schema already documents the slug parameter. The description earns credit by adding real-world semantics: slug format via example ('/tools/acme-writer' -> 'acme-writer') and provenance ('as returned by search_tools' toolUrl'). This goes beyond a baseline of 3 by teaching the agent where valid values come from.
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?
"Fetch the full public detail for one AI tool by its listing slug" uses a specific verb+resource and immediately differentiates from the sibling search_tools by emphasizing 'full public detail.' The description also specifies the exact data returned (launch date, revenue signals, for-sale status), leaving no ambiguity about what the tool does.
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 sequences usage with 'Call this after search_tools,' names search_tools as the source of the slug, and clarifies behavior for invalid slugs ('Returns null if the slug doesn't resolve'). This provides clear when-to-use context and names the alternative tool it works with, effectively covering the when/alternatives criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_alternativesList alternatives to a toolAList live, same-category alternatives to a well-known AI tool (e.g. 'chatgpt', 'notion-ai', 'figma'). Call this when a user asks 'what are the alternatives to X' or wants competitors/substitutes for a named product. target is the well-known tool's slug, not a listing slug from this catalog.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | Slug of the well-known target tool, e.g. 'chatgpt'. |
| Name | Required | Description |
|---|---|---|
| target | Yes | null when the target slug is not a known product. |
| alternatives | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and idempotent behavior, lowering the burden. The description adds context about 'live, same-category' and clarifies that the target is a well-known slug, not a listing slug, which is useful behavioral detail. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action and resource, then gives examples, a trigger condition, and a critical param clarification. Every sentence earns its place with 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 single-parameter tool with a clear output schema, the description covers the trigger, the object, and the differentiating constraint. It is complete enough for an agent to correctly invoke the tool without ambiguity.
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 for target is 100%, so baseline is 3. The description adds value by clarifying that target is a well-known slug and not a listing slug, which prevents a common misuse. This extra disambiguation justifies one point above baseline.
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 live, same-category alternatives to a well-known AI tool, with concrete examples. It distinguishes from siblings like search_tools or get_tool by focusing on alternatives rather than general search or retrieving a single 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?
It explicitly says to call this when a user asks for alternatives or competitors, which is strong usage guidance. However, it doesn't explicitly contrast with sibling tools like search_tools, so the when-not guidance is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_offersList paid add-ons (offers)AList the optional paid add-ons a maker can buy to promote their listing (name, one-sentence effect, price in USD). Listing is always free; these only ADD reach and are always labeled 'Sponsored'. Call this to see what's purchasable before create_checkout. During free early access (early-bird), offers are not yet purchasable and this says so (purchasable=false).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| note | Yes | |
| site | Yes | |
| offers | Yes | |
| purchasable | Yes | False during free early access — nothing can be bought. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and idempotentHint annotations, the description adds meaningful behavioral context: listings are always free, offers only add reach, are labeled 'Sponsored', and during early access the tool reports purchasable=false. This is rich, non-obvious behavior that helps the agent set expectations.
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 three sentences and front-loaded: the first sentence states the tool's purpose and output fields, the second clarifies cost/visibility semantics, and the third gives usage guidance and the early-access caveat. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter list tool with annotations and an output schema, the description is complete: it explains what is returned, why and when to call it, the relationship to create_checkout, and a special mode (early access). No important behavioral or usage aspects are 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?
The tool has zero parameters, and the schema is fully covered by the empty properties object. The description does not need to explain parameters; the baseline of 4 for a zero-parameter tool 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 and resource: 'List the optional paid add-ons...'. It clearly distinguishes this from siblings by focusing on paid add-ons/offers and explicitly relates it to create_checkout ('Call this to see what's purchasable before create_checkout').
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 clear usage context: 'Call this to see what's purchasable before create_checkout.' It also explains the early-access exception. However, it does not explicitly state when not to use this tool or mention alternatives like list_alternatives, so it stops short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_toolsSearch AI toolsASearch the Alternativas IA catalog of live, published AI tools by a free-text query. Matches on tool name, tagline, or category name (case-insensitive substring). Call this first when a user asks to find, discover, or compare AI tools by keyword, use-case, or category (e.g. 'agents', 'writing assistants'). Returns up to 20 results.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Free-text search term, e.g. a tool name, use-case, or category. |
| Name | Required | Description |
|---|---|---|
| results | Yes | Matching live listings, newest first, capped at 20. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the description need not restate safety. It adds valuable behavioral detail beyond the annotations: case-insensitive substring matching on specific fields, scope limited to 'live, published' tools, and a 'Returns up to 20 results' limit. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with front-loaded primary action, followed by match behavior, result limit, and usage guidance. Every sentence adds distinct information with no redundancy or 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?
For a simple one-parameter search tool with an output schema present, the description fully covers purpose, when to use it, matching behavior, scope, and result cap. The output schema removes the need to explain return values, so no meaningful gap remains.
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 query parameter already has a description. The tool description enriches the parameter semantics by clarifying exactly what the free-text query matches against ('tool name, tagline, or category name') and that matching is case-insensitive and substring-based, which goes beyond the schema's generic example.
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+resource pair ('Search the Alternativas IA catalog of live, published AI tools') and clearly defines the match targets ('tool name, tagline, or category name'). It further distinguishes itself from siblings by stating 'Call this first when a user asks to find, discover, or compare AI tools', setting it apart from list/get/create 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 explicitly says when to use the tool ('Call this first when a user asks to find, discover, or compare AI tools by keyword, use-case, or category') with examples. It does not explicitly name when-not-to-use or alternatives, but the instruction to call it first provides strong situational guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_toolSubmit a new AI toolAInspectSubmit a new AI tool to Alternativas IA on behalf of its maker. Creates a draft listing (not yet public) and returns a claimUrl the maker must visit to sign up and claim/publish it. Call this only when a user explicitly wants to list their own tool — never to submit a tool on someone else's behalf without their email. Rate-limited globally to 5 submissions per hour. Supply tagline, problemSolved, logoUrl and imageUrls whenever you can: they are what make the published listing look like a real product page rather than a stub.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The tool's https homepage URL. | |
| name | Yes | The tool's name. | |
| Yes | The maker's email — used to claim the listing on signup. | ||
| logoUrl | No | https URL of the tool's logo. We copy it to our own storage — we never hotlink the maker's host. | |
| tagline | No | One line shown on every card (max 60 chars). Supply it — without one we fall back to a truncated description. | |
| xHandle | No | X/Twitter handle, without the @. | |
| category | Yes | Category slug, e.g. 'agents', 'writing', 'coding', 'design', 'marketing', 'productivity', 'video', 'analytics'. | |
| imageUrls | No | Up to 5 https screenshot URLs, best first. Copied to our own storage. Without any, the listing shows a generated placeholder card instead of a real screenshot. | |
| description | Yes | A description of the tool (40-1000 chars). | |
| problemSolved | No | What problem the tool solves, in the maker's words (max 160 chars). Rendered as its own block on the listing page. | |
| xHandleIsFounder | No | True when the handle is the founder's personal account rather than the product's. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| error | No | |
| claimUrl | No | Where the maker signs up to claim the DRAFT. Present only when ok is true. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even though annotations already indicate a non-read-only, non-idempotent mutation, the description adds meaningful behavior: the submission creates an unpublished draft, the maker must claim it via a returned claimUrl, and there is a global limit of 5 submissions per hour. This is useful operational context beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core purpose, and every sentence earns its place: what the tool does, when it is allowed, and which optional params matter. There is no redundant or vague 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?
The description covers the full workflow—draft creation, claim process, and rate limiting—and the input schema covers parameter details. Since an output schema exists, the description does not need to restate return values, but it still clearly identifies the key output field claimUrl.
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 parameters already carry clear meaning. The description adds value by telling agents to provide tagline, problemSolved, logoUrl, and imageUrls whenever possible because they make listings look like real product pages instead of stubs.
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 names a specific verb and resource: 'Submit a new AI tool to Alternativas IA on behalf of its maker.' It also distinguishes this from read/list/sibling tools by explaining that it creates a draft listing and returns a claimUrl, not a public listing.
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 an explicit when-to-use condition: 'Call this only when a user explicitly wants to list their own tool.' It also provides a clear exclusion: never submit a tool on someone else's behalf without their email, which prevents common misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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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
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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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