A real worked example: nil turnover, a director loan, a near-empty bank account, the simplest accounts the UK regime permits. Use it to demonstrate check_accounts or as a template for a first-year company. Amounts in major units.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior, so the bar is lower. The description adds useful content-level context about what the sample contains and that amounts are in major units, though it does not describe any operational traits beyond the 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?
The description is three tight sentences with no wasted words. It front-loads the sample's identity and contents, then states usage, then adds the unit note.
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 zero-parameter, output-schema-less sample fixture, the description provides enough context: what the sample contains, what it is for, and the units used. The annotations cover the safety profile, so nothing essential is missing.
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 tool takes zero parameters, so per the rubric the baseline is 4. The description's note about amounts in major units adds data-format context, but there are no input parameters to clarify.
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 identifies the resource as a worked sample accounts fixture and details its contents (nil turnover, director loan, near-empty bank), which clearly distinguishes it from live-data siblings like check_accounts. An agent can tell it provides a static example rather than performing analysis.
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?
It explicitly says to use it to demonstrate check_accounts or as a template for a first-year company, giving clear intended contexts. It does not state when not to use it or name a competing alternative beyond check_accounts, so it falls short of the top score.
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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