company_number_formatValidate UK Companies House number format (not existence).
| Name | Required | Description | Default |
|---|---|---|---|
| company_number | Yes | 8 digits, or 2 letters + 6 digits. |
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | ||
| scope | No | ||
| valid | Yes | ||
| reason | Yes | ||
| country | No | ||
| normalized | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and idempotent; the description adds an important behavioral boundary by stating format-only validation and excluding existence checking. This helps the agent set expectations and distinguishes this operation from lookup or verification tools.
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 that states the exact operation and its limitation without wasted words. Every word contributes to understanding the tool’s purpose and boundary.
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 one fully documented parameter, a provided output schema, and annotations covering read-only and idempotent behavior, the description is complete for safe invocation. The explicit 'not existence' qualification closes the main ambiguity, making the tool self-sufficient.
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 schema already describes the single parameter fully as '8 digits, or 2 letters + 6 digits', giving 100% schema coverage. The description adds no additional parameter detail, but none is needed 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?
Description uses a specific verb ('Validate') with a clearly identified resource ('UK Companies House number format') and clarifies it does not check existence. Among the sibling validation tools, this uniquely targets Companies House format, so an agent can distinguish it from e.g., ein_format_check or isbn_check.
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 the correct use case: validating UK company number format, not entity existence. It does not explicitly name an alternative or state when-not-to-use, but the clear scope ('not existence') provides sufficient contextual guidance for an agent.
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 identifier type or specific function (e.g., IBAN vs. EIN vs. block info), with no overlap even among similar validation routines. The batch and rental tools combine distinct sub-checks without ambiguity, making misselection unlikely.
Tool names are uniformly snake_case and mostly follow a descriptive pattern, but the action verbs vary (e.g., 'check', 'format', 'info', 'guard', 'verdict') rather than a single verb_noun structure. While clearly readable, the pattern is not perfectly uniform.
At 28 tools, the server feels like a broad utility pack rather than a focused domain. The count exceeds the 'heavy' threshold and includes three distinct sub-domains (financial identifiers, rental fraud, on-chain queries), making the surface area hard to navigate coherently.
The identifier validation coverage is thorough (most common checksums), rental checks cover risk, deposit, and verdict, and on-chain tools handle balances, activity, and settlement history. Minor gaps exist (e.g., no token transfer history, no generic identifier detection) but agents can likely work around them.