get_productFetch full product detail by slug — specs, EMI options, warranty, current price, stock. Use after search_products when the user wants to dig into one item.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug from a search result, e.g. "sony-bravia-x90l-65" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the behavioral disclosure burden. It clearly indicates a read-only fetch operation and lists the data returned (specs, EMI options, warranty, current price, stock). It does not mention error handling or non-finding behavior, but for this straightforward read operation, the disclosure is adequate and 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?
The description is two sentences, front-loaded with the core purpose, and includes the usage guidance in a compact, high-signal format. Every phrase adds value, with no redundancy or fluff.
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 having no output schema or annotations, the description effectively conveys the complete context needed: it lists the returned data fields, specifies the input source, and sequences usage after search_products. For a simple one-parameter read tool, this is sufficiently complete for reliable invocation.
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 the required parameter 'slug' with a description and example, giving 100% schema coverage. The description adds valuable context by explaining that the slug originates from search_products results, which helps the agent understand how to obtain and use the parameter correctly beyond just the schema 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 clearly states the tool's function with a specific verb ('Fetch') and resource ('full product detail by slug'), and enumerates the included fields (specs, EMI, warranty, price, stock). It also distinguishes itself from siblings by explicitly positioning it as the follow-up to search_products for deeper detail.
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 when to use this tool: 'Use after search_products when the user wants to dig into one item.' This provides clear context and implicitly contrasts with the sibling search_products, giving the agent unambiguous guidance on tool selection.
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.
Each tool targets a distinct facet: physical locations, product detail, category browsing, and full-text search. There is no overlap in purpose, and the descriptions reinforce clear boundaries. An agent can easily select the correct tool for any user intent.
All tool names follow a consistent verb_noun pattern with lowercase snake_case (find, get, list, search). The only mild deviation is 'find_showrooms_near' which still fits the pattern. This predictability aids agent selection and reduces confusion.
Four tools is a well-scoped count for a storefront/MCP server. Each tool serves a distinct core function without bloat or redundancy. This is within the ideal 3-15 range and feels neither thin nor overweighted.
The set covers product discovery, detail, and store location, which are the primary use cases. A possible gap is the lack of a direct 'list products by category' tool, though search can partially compensate. Overall, the surface is logically complete for browsing and comparison.