get_recommendationGiven a problem statement (and optionally a stack/company size), return 1-3 best-fit products with reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| stack | No | Tech stack or category they already use (e.g., 'OpenAI + Pinecone + Vercel') | |
| problem | Yes | Problem the user is trying to solve, in their own words | |
| company_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It states output range ('1-3 best-fit products with reasoning') but does not disclose how selections are made or any limitations (e.g., data freshness, bias). This is adequate but 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?
The description is a single, front-loaded sentence with no wasted words. Every word carries meaning.
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 and 3 simple parameters, the description covers the core functionality. It could elaborate on return structure but is largely sufficient for this tool's complexity.
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 67%, and the description echoes parameter names ('problem statement', 'stack/company size') without adding significant depth. It does not define the enum values or expected formats beyond the 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?
The description clearly specifies the action ('recommend'), the resource ('products'), and the input ('problem statement'), distinguishing it from sibling tools like search_products or compare_products.
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 clear context for when to use the tool (solving a problem with optional stack/size), but does not explicitly state when not to use it or mention alternatives.
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 aspect of the commerce and affiliate ecosystem: comparison, checkout, affiliate info, cross-sells, free tier, MCP install, pricing, recommendation, trust score, registration, partnership, consultation, search, and vendor verification. No two tools have overlapping purposes; boundaries are clear.
Tool names follow a consistent verb_noun pattern in snake_case (e.g., compare_products, get_affiliate_info, request_partnership). While the verbs vary, the structure is predictable and uniform, with no mixing of conventions.
14 tools is well-scoped for a commerce marketplace server covering product discovery, comparison, pricing, affiliate management, checkout, partnerships, and vendor verification. Each tool serves a clear purpose without bloat.
The tool set covers the full lifecycle from product discovery to checkout, including affiliate management and partnerships. Minor gaps exist, such as lacking a dedicated tool for retrieving detailed product information beyond search results, but agents can work around this.