hardware_detailsOne graphics card or machine in a market: its specifications, every current price (new and used), and which models it runs fully in memory at a context length, how fast.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | A card or machine slug, from find_hardware | |
| market | No | Where to price it: us, uk, de (Germany and the rest of the EU) or ca (Canada). | us |
| context | No | Context length in tokens: 8192, 32768, 65536 or 131072. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, closed-world behavior, and the description adds the substantive detail that the result includes every current price (new and used) and per-model memory-fit and speed figures. With no output schema, this disclosure of return content carries real weight; it only stops short of stating price freshness, currency, or pagination.
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?
A single dense sentence, front-loaded with the resource and then the three things returned. Nothing is wasted, though the trailing 'how fast' is terse enough to be slightly ambiguous about what is being measured.
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?
With no output schema, the description compensates by describing the returned data (specs, prices, compatibility, speed) and the schema fully covers parameters, while annotations cover the safety profile. An agent has everything needed to invoke it correctly.
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%, so all three parameters (slug provenance, market enum, context-length enum) are already fully documented. The description gestures at 'in a market' and 'at a context length' but adds no syntax, defaults, or constraints beyond what the schema states, so 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 names a specific resource (one graphics card or machine) and enumerates the payload: specifications, current new and used prices, and which models fit fully in memory plus speed. That is well beyond the title 'Hardware details' and separates it from search-style siblings, though it never names those siblings explicitly.
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?
Usage is only implied: the schema notes the slug comes from find_hardware, so the lookup-after-search flow is inferable, but the description itself says nothing about when to use this versus compare_hardware, best_hardware, or hardware_for_model, and gives no exclusions.
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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