ein_format_checkValidate US Employer Identification Number FORMAT (NN-NNNNNNN). No public EIN checksum exists.
| Name | Required | Description | Default |
|---|---|---|---|
| ein | Yes | US EIN, 9 digits; dashes/spaces allowed. |
| 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 provide readOnlyHint and idempotentHint, so safety and repeatability are covered. The description adds meaningful domain context: EIN has no public checksum, so the tool can only validate format, not authenticity or correctness beyond structure. This is useful behavioral disclosure 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?
Two short sentences, with the core purpose and format front-loaded and the critical limitation stated in the second sentence. There is no redundant or filler content; every word earns its place.
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 single-parameter, read-only, idempotent format validator with a provided output schema and full schema coverage, the description is fully adequate. It states what is validated, the expected format, and the key limitation, leaving no significant gap for correct invocation.
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 100%, so the description is not required to compensate for undocumented parameters. The description's 'NN-NNNNNNN' pattern adds a concrete format illustration, but the schema already states '9 digits; dashes/spaces allowed.' Thus the description adds minimal additional semantic value 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 states a specific verb-resource pair: 'Validate US Employer Identification Number FORMAT', with the expected pattern 'NN-NNNNNNN' explicitly given. It also distinguishes itself from checksum-style validation by noting that no public EIN checksum exists, which differentiates it from sibling checksum tools.
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 clearly scopes the tool to US EIN format checking and explicitly warns that no checksum validation is possible, effectively telling the agent what this tool cannot do. It does not explicitly name sibling alternatives, but the context and the no-checksum caveat give clear usage direction.
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 data source: every checksum validation is for a specific standard (IBAN, LEI, VAT, etc.), and the on-chain and rental tools are clearly separate domains. There is no ambiguity between tools; even similar-sounding checks like 'luhn_check' and 'ean13_check' are distinct algorithms with explicit descriptions.
Most validation tools follow a 'X_check' pattern (iban_check, swift_bic_check, ein_format_check), but others deviate: 'company_number_format' uses a noun_noun structure, 'block_info' is noun_noun, and 'batch_validate' and 'context_distill' use verbs. While the pattern is not uniform, the names are readable and the convention is understandable, just not fully consistent.
With 28 tools, this server exceeds the typical 3-15 well-scoped range and even the 16-25 heavy range. The server covers multiple unrelated domains (identifier validation, blockchain queries, rental fraud, text processing), which inflates the count. While each tool is individually justified, the overall surface feels overly broad for a single MCP server.
For each sub-domain, coverage is strong: identifier validation includes a wide range of international standards, on-chain queries cover balances, activity, and settlement history, and rental tools provide risk, verdict, and deposit guard. Minor gaps exist (e.g., no country-specific tax IDs beyond what's listed, no transaction history for arbitrary tokens), but the visible coverage is comprehensive for the stated purposes.