email_validate
Validate email address format. When: Syntax-check a single email address.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| Yes |
Validate email address format. When: Syntax-check a single email address.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It only states 'Validate email address format' and 'Syntax-check', but does not specify what validation entails (e.g., format rules), how results are returned, or responses for invalid inputs. This is insufficient for an agent to predict behavior reliably.
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 with one sentence containing the essential information. It avoids unnecessary words, earning high marks for conciseness. However, it could be slightly more structured (e.g., separating different aspects) without increasing length significantly.
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 the low complexity (one string parameter, no output schema), the description provides the basic purpose but lacks details about return format, error handling, or validation scope. It is sufficient for a trivial tool but could be more informative for an agent to handle edge cases.
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 has 0% description coverage, and the description adds no extra meaning beyond the parameter name 'email'. The phrase 'single email address' merely restates the obvious. The description fails to compensate for the missing schema descriptions, offering no additional semantic value.
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 verb 'Validate' and the resource 'email address format', and specifies the scope as a single email address. This distinguishes it from sibling tools like 'extract_emails' or other validation tools, making its purpose unambiguous.
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 includes a 'When:' clause indicating it is for syntax-checking a single email address. While it lacks explicit when-not-to-use instructions or alternatives, the context (no similar sibling for email validation) makes the usage clear.
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.
Every tool has a clear, distinct purpose with thorough descriptions. Even closely related tools like base64_decode/encode and hash_md5/sha256 are easily differentiated by name and description.
All tools follow a consistent lowercase_underscore naming convention, typically in a <domain>_<action> or <action>_<domain> pattern. There are no jarring deviations or mixed styles.
193 tools is an extreme count, far beyond what any focused server needs. While each tool has utility, the sheer number creates a kitchen-sink effect that overwhelms agents and hinders discoverability.
Within each subdomain (JSON, cron, JWT, etc.), the coverage is exhaustive, covering validation, conversion, parsing, and more. Minor gaps exist (e.g., YAML-to-TOML conversion missing), but overall it is remarkably complete.