Channel3 Product Search
Server Details
Product search and price comparison for AI agents. Search 100M+ products across thousands of retailers by text or image, compare live offers and prices, and turn any merchant URL into structured product data. No API key required to start; add one for unlimited use and affiliate commission.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.8/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one searches for products, the other retrieves details for a specific product. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern in snake_case: search_products and get_product. This makes the tool intentions clear and predictable.
With only 2 tools, the server's surface is minimal. For a product search domain, this could be acceptable for a narrow scope, but it feels thin compared to typical search APIs that include pagination, filtering, or listing.
The server covers the core search and retrieval use cases. However, it lacks pagination, sorting, or explicit filtering options, which are common gaps for a search-focused toolset.
Available Tools
2 toolsget_productGet ProductARead-onlyInspect
Get full details for one product. Pass a product ID (from a search_products result) or URL.
| Name | Required | Description | Default |
|---|---|---|---|
| product_id | Yes | Product ID (from a `search_products` result) or URL. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, so the description does not need to disclose read-only behavior. It adds no behavioral context beyond the annotation, achieving adequate transparency.
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-loaded with purpose, and no wasted words. Every sentence 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?
The tool is simple with one parameter and no output schema. The description fully covers what an agent needs: purpose, source of input, and relation to sibling. No gaps.
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 the single parameter, which matches the description. The description adds no extra meaning beyond the schema, meeting the 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?
Description clearly states 'Get full details for one product' with a specific verb and resource. It distinguishes from the sibling tool 'search_products' by specifying it handles one 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?
Provides clear context on when to use: after a search, using a product ID or URL. Does not explicitly state when not to use, but the implied scenario is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsSearch ProductsBRead-onlyInspect
Search products by natural-language query. The query can be as specific as you need.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Natural-language product search. Include any constraints inline (brand, retailer, category, color, size, price range, gender, etc.). Required unless `image_url` is given. | |
| image_url | No | Public image URL for visual search. Combine with `query` for text+image search. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, indicating a read operation. Description adds no extra behavioral context beyond 'search', so score is low.
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, front-loaded with essential information, no wasted 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 simple input schema (2 params, fully described) and no output schema, description is adequate. Lacks details like result format or pagination, but acceptable 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 100%, providing full parameter details. Description mentions query specificity and conditional requirement, but adds no meaning beyond schema.
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 action 'Search' and resource 'products' with natural-language queries. Distinguishes from sibling 'get_product' which likely retrieves a single 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?
No guidance on when to use this tool vs. alternatives like 'get_product'. Does not specify exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$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.
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