Checks value is a single, bare RFC 5322 email address — syntax only, not a deliverability check. user@localhost is valid (no TLD required). "Name " display syntax is rejected.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | identifier or value to check |
| Name | Required | Description | Default |
|---|---|---|---|
| valid | Yes | ||
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden to disclose behavioral traits. It thoroughly covers key behaviors: syntax-only validation, acceptance of addresses without TLD, and rejection of display-name syntax. No contradictions are present.
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 extremely concise at two sentences. The first sentence front-loads the core purpose and scope. Every word serves a purpose, with no fluff or repetition.
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?
Given it is a simple single-parameter tool with full schema description, an explicit output schema, and straightforward purpose, the description is complete. It clarifies the validation algorithm (RFC 5322, syntax only) and key boundary cases, leaving no ambiguity for an AI agent.
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% (the single parameter 'value' is described as 'identifier or value to check'). The description adds important context about the expected format (bare RFC 5322 email, not a display name), which helps the agent understand what input to provide. However, it does not elaborate on edge cases like whitespace handling or case sensitivity, which could further enhance understanding. Baseline 3 is appropriate.
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 the tool checks if a value is a single, bare RFC 5322 email address. It specifies the scope is syntax-only, not deliverability, which helps distinguish from other validation tools. The examples of valid ('user@localhost') and invalid ('Name <email>') cases further clarify the exact boundary.
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 implicitly guides when to use this tool via the 'syntax only, not a deliverability check' clause, suggesting it is for format validation, not for verifying an email exists. It does not explicitly state when not to use it or compare to siblings, but the context of siblings (jsonschema_validate, regex_match, url_validate) and the clear scope make misuse unlikely without further elaboration.
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 validation domain: email, URL, regex, and JSON schema. There is no overlap; an agent can clearly distinguish which tool to use for a given validation task.
All tools follow a consistent verb_noun snake_case pattern (e.g., email_validate, url_validate). The use of 'match' for regex is a minor variation but still fits the pattern, and overall naming is predictable.
Four tools is a well-scoped number for a validation utility server. It covers the main advertised domains without being bloated or too sparse.
The server covers its named areas (regex, email, URL) and adds JSON schema validation. Missing a generic string pattern validator or phone number validator, but the scope is sufficiently covered for common use cases.