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?
Annotations already declare readOnlyHint and idempotentHint, so safety is covered. The description adds meaningful context by scoping behavior to syntax checking and common domain typos, which clarifies that it is not 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?
A single, front-loaded sentence communicates the full purpose with a helpful example and no wasted words. It is compact and immediately scannable.
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 one-parameter, read-only, idempotent tool with an output schema, the description fully supports correct selection and invocation. Nothing essential is missing for an agent to understand what this tool does and how to call it.
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 documents the only parameter at 100% coverage, so the description adds no additional parameter-level detail. The description does clarify what will be checked against the email, but the schema carries the semantic load.
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 names the exact verb and resource: 'Validate email address syntax' plus the extra typo-detection behavior with a concrete example. It clearly distinguishes this from sibling validation tools like validate_iban by explicitly targeting email addresses.
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 implies when to use the tool: whenever an email address needs syntax validation or common domain typo detection. It does not explicitly name alternatives or exclusion criteria, but the email-specific scope makes the intended use unambiguous.
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 targets a distinct resource or action, and the detailed descriptions clearly separate near neighbors like generate_test_bsn versus generate_brp_test_data, read_page versus url_screenshot versus url_to_pdf, and image_compress/convert/resize. Even with 40 tools, there is no real boundary-blurring overlap.
All names are snake_case and readable, but the set mixes conventions: verb_noun (generate_*, validate_*), noun_verb (pdf_merge, image_resize), conversion-style names (csv_to_json, html_to_pdf), and bare nouns (base64, qr_code_png). The groups are recognizable, but there is no single predictable pattern.
Forty tools is an oversized surface for an agent to consider on every call, well above the point where tool selection cost starts to hurt. The broad purpose explains the count, but many one-off utilities could be grouped or exposed selectively.
The server covers many domains—encoding, Dutch test data, image/PDF handling, memory, and workflows—but several categories are partial: there are no reverse conversions like json_to_csv or html_to_markdown, no PDF text extraction, and no workflow create/update/delete tools. Agents can work around some gaps, but notable operations are missing.