AI shopping search: real, in-stock US and Amazon products with prices, ratings and buy links.
With only one tool, there is no ambiguity. The tool's purpose is clearly defined as searching for products, and no other tools exist to potentially overlap or confuse.
The single tool uses a clear verb_noun pattern ('search_products'), which is consistent. Since there is only one tool, naming conventions are perfectly uniform.
The server has exactly one tool, which is on the low end. While a focused search service can function with a single tool, typical well-scoped servers have 3–15 tools, making this feel thin but not unreasonable.
The tool provides comprehensive search results including price, rating, stock, affiliate link, and markdown output. However, the server lacks any additional tools for filtering, sorting, or retrieving product details, which are minor gaps for a more advanced search experience.
Search Perkusai for real, in-stock products in amazon.com (prices in US dollars).
Describe what the shopper wants in natural language - e.g. "a full-size basketball", "espresso machine under $200", "wireless earbuds for running". Returns
matching products with title, price, rating, stock, market and an affiliate `buy_url` (present
it as the purchase link - the price is unchanged for the buyer), plus a `guide`, `coverage`
and a paste-ready `markdown` block.
Args:
query: The shopper's request, in natural language.
k: How many products to return (1-12).
lang: Response language hint. This catalogue is served in English.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | How many products to return (1-12). | |
| lang | No | Response language hint. This catalogue is served in English. | en |
| query | Yes | The shopper's request, in natural language. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, openWorldHint, and destructiveHint. The description adds value beyond these by detailing the return fields (title, price, rating, stock, market, affiliate buy_url, guide, coverage, markdown block) and emphasizing that the price is unchanged for the buyer. It also describes the real/in-stock nature of results. No contradictions exist.
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 well-structured, front-loading the core purpose, then usage guidance, then return details, then parameter list. It is slightly verbose but every sentence adds value. The length is appropriate for the tool's complexity.
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 presence of an output schema (not shown) and comprehensive annotations, the description covers all necessary aspects: purpose, parameters, usage examples, return format, and special notes (affiliate link, markdown block). It is fully sufficient for an agent to understand 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?
Schema coverage is 100% with solid descriptions. The description adds value by giving natural language examples for the query parameter and repeating the constraints for k and lang. While it mostly restates schema info, the examples for query enhance understanding beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for real, in-stock products on Perkusai/amazon.com with prices in USD. It uses a specific verb+resource combination ('Search Perkusai for real, in-stock products') and includes examples of natural language queries. With no siblings, no differentiation is needed, but the purpose is 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 explains how to use the tool: instructs the agent to 'Describe what the shopper wants in natural language' and provides examples. It also specifies the source domain and currency. Since there are no sibling tools, explicit when-to-use vs. alternatives is not required, but the guidance is clear and actionable.
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.
search_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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