validate_emailValidate email address syntax and detect common domain typos (e.g. gmial.com).
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The email address to check |
| Name | Required | Description | Default |
|---|---|---|---|
| No | |||
| local | No | ||
| valid | No | Whether the input passed every check | |
| domain | No | ||
| result | No | The result, when it is not an object | |
| warnings | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / email / descriptionAdded value: +"The email address to check"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "domain": {
+ "type": "string"
+ },
+ "email": {
+ "type": "string"
+ },
+ "local": {
+ "type": "string"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ },
+ "valid": {
+ "description": "Whether the input passed every check",
+ "type": "boolean"
+ },
+ "warnings": {
+ "items": {},
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context by clarifying that validation covers syntax and common typos, which implies a local, deterministic check rather than a deliverability or mailbox-existence check.
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 compact sentence that front-loads the core purpose and uses a concrete example ('gmial.com') to illustrate the typo-detection behavior. 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 simple single-parameter, read-only validator with an output schema, the description covers the relevant behavior and input requirements sufficiently. The only potential omission is a note about not checking mailbox existence, but this is reasonably implied by emphasizing 'syntax' and 'typos'.
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 input schema fully documents the single 'email' parameter with a clear description, so schema coverage is 100%. The tool description adds no additional parameter-level information, so the baseline score of 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 uses a specific verb ('validate'), a specific resource ('email address'), and adds a concrete secondary behavior ('detect common domain typos'). This makes the tool's purpose unmistakable and clearly distinguishes it from siblings like validate_iban.
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?
There is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. The usage context is implied through the clear purpose, but an agent must infer when this tool is the right choice.
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.
Most tools target a clearly distinct resource and action, so an agent can usually tell them apart. A few close pairs exist (generate_test_bsn vs generate_brp_test_data, html_to_pdf vs url_to_pdf, read_page vs url_screenshot), but the descriptions draw clear boundaries.
Tool names consistently use snake_case and mostly follow a verb_noun pattern like generate_slug, validate_email, or pdf_merge. There are a few noun-style exceptions such as base64, csv_to_json, and password_strength, but no mixed casing or chaotic naming.
40 tools is heavy and exceeds the comfortable selection range for most agents. The server presents itself as a general-purpose utility toolbox, so the breadth is defensible, but the large flat tool list creates real navigation burden.
The toolkit covers common encoding, conversion, image/PDF, validation, Dutch-specific test data, memory, and workflow needs quite well. Minor gaps like PDF text extraction or JSON-to-CSV conversion exist, but agents can typically work around them.