get_productGet a single brand/product's record across every category it is ranked in, with verdicts and its homepage.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug, e.g. langfuse |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral context about the return data structure (count, category rankings, verdicts, homepage), which goes beyond annotations.
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?
Description is a single, well-structured sentence with no redundancy. It front-loads the core action and key details efficiently.
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 parameter and no output schema, the description sufficiently explains what the tool returns and its scope, fully covering necessary context.
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% for the single slug parameter. The description does not add extra meaning beyond what the schema provides (e.g., example value). Baseline 3 applies.
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 gets a single brand/product's record across all ranked categories, including verdicts and homepage, distinguishing it from siblings like search_best or get_best_in_category.
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 implies when to use (when needing a product's full record), but does not explicitly differentiate from siblings or provide when-not-to-use guidance. It relies on the agent inferring usage from purpose.
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 operation: listing categories, searching, getting a category leaderboard, polling history, or product details. There is no functional overlap, ensuring an agent can clearly differentiate between them.
All tools follow a consistent verb_noun pattern (get_best_in_category, get_poll_history, get_product, list_categories, search_best), with clear verbs and descriptive nouns, promoting predictable usage.
With 5 tools, the server is well-scoped for a domain focused on ranking retrieval and exploration. Each tool serves a clear purpose without unnecessary bloat or inadequacy.
The tool set covers the full workflow: discover categories (list_categories), search for best (search_best), view detailed rankings (get_best_in_category), audit trail (get_poll_history), and cross-category product info (get_product). There are no obvious gaps for the intended use case.