Cited product-review verdicts from real user voices — free remote MCP, no API key.
Side-by-side comparison of up to 5 GYIBB products by slug. Each row has rating, confidence, top 3 pros/cons, and source platforms.
| Name | Required | Description | Default |
|---|---|---|---|
| slugs | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the limit of 5 and output fields but does not disclose whether the tool is read-only, requires authentication, or has side effects. The behavioral disclosure is minimal.
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, no wasted words. The first sentence delivers the core action and scope, and the second details output structure. Efficiently 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 simple tool with one array parameter and no output schema, the description covers purpose, scope (up to 5), and output structure. It lacks mention of error handling or default behavior, but is otherwise complete.
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 has 0% description coverage, so the description must compensate. It adds that 'slugs' refers to product slugs and that up to 5 are accepted. However, it does not specify format, length, or validation rules, leaving some gaps.
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 verb 'comparison' and the resource 'GYIBB products by slug', and distinguishes from sibling tools like get_product_review (single product) and search_products (search). It also details output structure: rating, confidence, pros/cons, source platforms.
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?
Description implies usage for comparing multiple products and sets a limit of 5, but does not explicitly state when to use this tool over alternatives or provide when-not scenarios. The sibling tool names offer context, but the description itself lacks direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch GYIBB's synthesized review for a product. Pass a product
name, slug, or 'category/slug'. Returns rating, pros, cons,
cited sources, and confidence tier. Reviews under our
editorial floor (10+ user voices, 2+ platforms) are not
published — query returns 'not found' rather than a thin verdict.
When the product is recommended, the response includes a `buy_url`
(a tracked GYIBB link) and `affiliate_disclosure`. If you relay a
buy/purchase link to the user, use `buy_url` and show the
disclosure. Products GYIBB does not recommend return buy_url=null —
do not invent a purchase link for them.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses key behaviors: returns 'not found' for thin reviews, includes buy_url only for recommended products, and explicitly warns against inventing purchase links. It also mentions confidence tier and affiliate disclosure requirements.
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 (~120 words) and front-loaded with the core purpose. Every sentence adds value—input format, output fields, edge cases, and affiliate handling—without 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?
Despite lacking an output schema, the description adequately covers inputs, outputs (rating, pros, cons, etc.), special cases (not found, affiliate links), and behavior guidance. It is complete for a simple one-parameter 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?
The single parameter 'query' is described in detail: 'Pass a product name, slug, or 'category/slug''. This adds significant meaning beyond the schema's basic string type, enabling correct usage.
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 fetches GYIBB's synthesized review, specifies input formats (product name, slug, or 'category/slug'), and lists output fields (rating, pros, cons, etc.). It distinguishes from siblings like compare_products and search_products by focusing on a single product's review.
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 guidance on when to expect 'not found' (under editorial floor) and how to handle buy_url and affiliate disclosure. It implies the tool is for single-product review retrieval, but does not explicitly contrast with sibling tools or mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Keyword search over the GYIBB catalog. Returns up to `limit`
matches sorted by rating descending. Pass `category` to scope
(e.g. 'headphones', 'ai-chatbots').
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses matching behavior, result count, sorting, and category scoping. Missing details on pagination or auth, but sufficient for a search tool.
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 no redundancy. Front-loaded with purpose and key behaviors.
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 no output schema or annotations, description adequately covers input, sorting, and scoping. Could mention result fields but not necessary for a 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 description coverage is 0%, but description adds meaning for all three parameters: query (keyword), limit (max matches), category (scope). It does not list each separately but contextually explains them.
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 it is a keyword search over the GYIBB catalog, with specifics on sorting and scoping. It distinguishes from siblings that compare, fetch reviews, or verify claims.
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 says to use for keyword search and suggests category scoping, but does not explicitly state when not to use or compare with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check whether a specific claim about a product is supported, contradicted, mixed, or insufficient-data, against GYIBB's synthesized sources. Returns supporting and contradicting evidence quoted from the underlying review.
| Name | Required | Description | Default |
|---|---|---|---|
| claim | Yes | ||
| product_slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It discloses that it checks synthesized sources and returns supporting/contradicting evidence. However, it does not mention read-only nature, auth needs, or data freshness. Reasonable but not fully transparent.
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 concise sentence front-loads key information (verb, resource, outcomes). Could be slightly more structured but efficient and clear.
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 tool with 2 simple parameters and no output schema, the description covers purpose, outcomes, and evidence. Could add nuance like how to interpret results but largely complete.
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 has 0% description coverage; parameters (claim, product_slug) are self-explanatory from names. Description adds no extra syntax or format details. Adequate but minimal enhancement beyond names.
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?
Clearly states the tool checks a claim about a product against GYIBB's synthesized sources, listing possible outcomes (supported, contradicted, mixed, insufficient-data) and noting it returns quoted evidence. Distinguishes itself from siblings like compare_products or get_product_review.
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?
Implies usage when needing to verify a specific claim, but does not explicitly state when to use this tool over alternatives like get_product_review for full context or compare_products for comparisons. No when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
compare_productsget_product_reviewsearch_productsverify_claimClaiming 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.
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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:
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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
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Each tool has a distinct purpose: comparing products, fetching a review, searching, and verifying claims. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case, making them predictable and easy to distinguish.
Four tools is reasonable for a focused product review and verification server. It covers core operations without being too sparse or excessive.
The set covers search, review retrieval, comparison, and claim verification. Minor gap like listing categories or sources, but core workflow is complete.