Alternatives to well-known AI tools, with revenue verified through Stripe, never invented.
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Average 4.6/5 across 6 of 6 tools scored.
Each tool targets a distinct action and resource: searching, fetching details, listing alternatives, listing offers, creating purchases, and submitting new tools. No two tools overlap in purpose, making selection unambiguous.
All tool names follow the same verb_noun snake_case pattern (create_checkout, get_tool, list_alternatives, list_offers, search_tools, submit_tool). The convention is predictable and uniform.
With 6 tools, the server is well-scoped for its purpose of discovering and submitting AI tools plus handling add-on purchases. Every tool has a clear role, and none are redundant.
The server covers core workflows: search, detail retrieval, alternative discovery, submission, offer listing, and checkout creation. Minor gaps exist (no update/delete for listings), but these are likely outsourced to the maker's claim flow.
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?
Beyond the annotations, the description adds crucial behavioral context: no real charge occurs until the human completes checkout in a browser, and certain conditions will return errors (listing missing, add-on not purchasable, payments not configured). This helps the agent understand external dependencies and consequences without seeing Stripe 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 sentences front-load the primary action and returned artifact, then immediately address the most important caveats and error conditions. Every sentence earns its place; no filler or unnecessary repetition.
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 only two parameters, 100% schema coverage, and an output schema, the description covers the central behavioral requirements: human interaction, payment delay, and expected failure modes. No significant missing context for selecting and safely invoking the 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 description need not repeat parameter syntax. It adds meaningful context by saying 'one paid add-on on one listing' and points to list_offers to map product enum values to their actual effects and prices, filling the gap left by the raw enum.
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: 'Create a Stripe checkout URL' for 'one paid add-on on one listing.' It clearly distinguishes this from browsing offers and other listing tools, and it scopes the action precisely.
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: the URL is created for a human to open, and the tool should not be treated as completing a purchase. It also points to list_offers for add-on details and confirms when errors will occur, but it does not explicitly compare against sibling tools or say when not to use this tool.
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 AI Alt Finder 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 indicate readOnlyHint, idempotentHint, and openWorldHint, so the description does not need to restate those. It adds valuable context beyond the annotations by disclosing the null return behavior for invalid slugs and listing the specific detail fields returned, which is helpful and non-obvious.
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 and immediately front-loads the purpose. Every clause earns its place: the slug source/example, the data returned, and the null behavior. No filler or 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?
Given the simple one-parameter schema, strong annotations, and an output schema, the description fully covers what the agent needs: when to call it, what input to pass, what it returns, and a failure mode. No meaningful gaps remain.
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?
Although the schema covers the slug parameter at 100%, the description adds crucial semantic detail: how to derive the slug from search_tools' toolUrl, including a concrete example ('/tools/acme-writer' -> 'acme-writer'). This goes well beyond the schema's generic 'The tool's listing slug.'
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 a specific verb ('Fetch') and resource ('full public detail for one AI tool by its listing slug'), and it distinguishes this tool from the sibling search_tools by defining the input as a slug from search_tools' toolUrl. The scope is precise and 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 explicitly instructs to call this tool after search_tools and explains what this tool provides that search_tools does not: full description, launch date, revenue signals, and for-sale status. This gives the agent a clear usage pattern and purpose relative to its sibling.
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 declare readOnlyHint and idempotentHint, so the description is not required to restate safety. It adds meaningful context about the tool returning 'live' and 'same-category' alternatives, which goes beyond the structured annotations. No contradictions 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: the first states the action and examples, the second provides usage and parameter clarification. Every sentence earns its place, no fluff, and the key information is front-loaded.
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 one parameter, explicit read-only/idempotent annotations, and an output schema, the description covers when to use it, how to interpret the parameter, and what it returns. It is fully sufficient for an agent to select and invoke the tool 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?
The schema already describes the single parameter 'target' with an example. The description adds crucial clarification that 'target' is specifically a well-known tool's slug, not a listing slug from the catalog. This prevents misuse and adds value beyond the schema's coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'list' and identifies the resource as 'live, same-category alternatives to a well-known AI tool'. It provides concrete examples ('chatgpt', 'notion-ai', 'figma') and clearly distinguishes from sibling tools by emphasizing it is about alternatives to a named product, not catalog listings.
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 explicit when-to-use context: 'Call this when a user asks what are the alternatives to X or wants competitors/substitutes for a named product.' It also clarifies the input format ('target is the well-known tool's slug, not a listing slug') but does not explicitly mention when not to use it or alternative tools.
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?
Annotations already indicate readOnly and idempotent behavior; the description adds valuable context beyond annotations: offers are always labeled 'Sponsored', listing is always free, and early-bird access results in purchasable=false. No contradiction found.
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 with no filler, front-loaded with the verb and resource, and the parenthetical field detail adds value without bloating the text.
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 zero-parameter, read-only listing tool with an output schema and strong annotations, the description fully covers purpose, workflow relationship to create_checkout, and the early-access caveat. No meaningful gaps remain.
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 coverage is trivially 100%, so the no-parameter baseline of 4 applies. No parameter explanations are necessary.
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 begins with a specific verb and resource — 'List the optional paid add-ons' — and clarifies exactly what is returned (name, effect, price). It also distinguishes from the sibling create_checkout by positioning this as the pre-checkout lookup 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 clearly says 'Call this to see what's purchasable before create_checkout,' giving an explicit workflow usage cue. It also notes an early-access exclusion where offers are not purchasable, though it does not explicitly contrast with other sibling tools.
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 AI Alt Finder 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 and idempotentHint, so the read-only nature is covered. The description adds valuable behavioral context beyond annotations: it specifies matching logic (case-insensitive substring on name, tagline, or category) and the result cap of 20 results. This augments the structured safety hints without contradicting them.
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 compact, well-structured paragraph that front-loads the core action, then elaborates on matching behavior and usage guidance. Every sentence adds value—no fluff or redundancy. It's appropriately sized for the tool's simplicity.
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 has only one simple parameter, an output schema, and strong annotations, the description covers all necessary aspects: what it does, how it matches, the result limit, and when to use it. There is no missing guidance for this straightforward 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% for the single query parameter, and the description essentially restates the same meaning ('Free-text search term'). The description does hint that the query matches specific fields, but that's more about tool behavior than parameter semantics. The schema already provides a clear type and example, so the description adds minimal additional parameter insight.
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 searches the AI Alt Finder catalog for AI tools by free-text query, matching on name, tagline, or category. This distinguishes it from siblings like get_tool (retrieves a specific tool) and list_alternatives (lists alternatives), making its 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 explicitly directs to call this tool first when a user asks to find, discover, or compare AI tools by keyword, use-case, or category. It provides clear context for when to use it, though it doesn't explicitly name alternatives or when not to use it, slightly below the highest bar.
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 AI Alt Finder 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?
With annotations being sparse (all false), the description adds meaningful context: it creates a non-public draft, returns a claimUrl, is globally rate-limited, and media fields are copied to storage rather than hotlinked. It does not cover failure/error behavior or rejection cases, but the core behavioral traits are disclosed well.
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 compact and front-loads the essential purpose, then layers the usage constraint, rate limit, and optional-field advice. There is no filler or repeated schema content.
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 purpose, usage constraints, rate limiting, important side effects (draft vs public, claimUrl, storage copying), and guidance on optional parameters. Combined with the detailed schema and sibling list, it gives an agent everything needed to decide when and how to call it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers all 11 parameter descriptions, so the baseline is 3. The description adds useful guidance on why certain parameters matter (tagline, problemSolved, logoUrl, imageUrls improve listing quality) and clarifies the claimUrl flow involving the email. That extra guidance distinguishes it from a schema-only definition.
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 states a specific verb+resource ('Submit a new AI tool to AI with maker on behalf of the maker'), explains it creates a draft and returns a claimUrl, and differentiates from siblings by emphasizing this is for listing one's own tool, not for generic search/get/list operations.
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 it (only when the user wants to list their own tool) and what not to do (never submit on someone else's behalf without their email). It also provides rate-limit context and practical advice on which optional fields to supply. This is strong usage guidance beyond just restating the tool's purpose.
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
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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