Fetch complete details for one product by id (e.g. roller-blockout, venetian-25mm-aluwood). Returns all available colours with in-stock status, materials, features, and maximum supported dimensions. Use before configure_product to confirm a colour exists and is in stock before committing.
| Name | Required | Description | Default |
|---|---|---|---|
| product_id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses a read-only fetch and enumerates return contents: colours, in-stock status, materials, features, and maximum dimensions. It also clarifies it is a pre-commit validation step. It does not discuss error behavior, but for a simple read tool this is adequate.
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 the action and object. Each sentence adds new information: what it fetches, what it returns, and when to use it.
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 one-parameter tool with no annotations and no output schema, the description is sufficient: purpose, input format, output content, and workflow with configure_product are covered. It could mention stock-only alternatives but is not incomplete.
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 and product_id is just a string. The description compensates by saying 'by id' and giving realistic slug examples (roller-blockout, venetian-25mm-aluwood), which clarify the expected ID format.
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 states 'Fetch complete details for one product by id' and gives concrete id examples. This clearly distinguishes it from search/lookup siblings by focusing on single-product retrieval.
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 explicit guidance: 'Use before configure_product to confirm a colour exists and is in stock before committing.' This gives a clear workflow context, though it does not explicitly list when not to use this tool versus alternatives like check_colour_stock.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but the presence of three search tools (search_catalog, search_products, lookup_catalog) and legacy tools (create_order, submit_enquiry) creates minor overlap. Descriptions help differentiate, but an agent might initially confuse them.
All 26 tool names follow a consistent verb_noun snake_case pattern (e.g., cancel_cart, configure_product). No mixed cases or irregular verbs are present.
26 tools is slightly high for a blind shop, but the domain requires many operations (search, pricing, cart, checkout, recommendations, etc.). Some legacy tools could be removed, but the count is still reasonable.
The tool surface covers the full customer journey from product discovery (search, recommendations) to purchase (cart, checkout, payment) and post-purchase (orders, swatches, price match, delivery estimates). No obvious gaps for the stated purpose.