Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web scraping validation.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Text to extract links from | |
| types | No | Types to extract (default: all three) |
| Name | Required | Description | Default |
|---|---|---|---|
| total | No |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "input": "nothing here"
+ }
+]Output schema / propertiesAdded value: +{
+ "total": {}
+}Output schema / (root)Previous value: -nullNew value: +{
+ "additionalProperties": true,
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds useful behavioral context beyond annotations by stating that results are 'categorized and deduplicated.' It does not disclose extraction edge cases, but the output schema likely carries return-shape details.
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?
Three short sentences with zero waste: purpose first, return behavior second, use cases third. The description is front-loaded and appropriately sized for a simple extraction tool.
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 two-parameter, read-only extraction tool with full schema coverage, annotations, and an output schema, the description gives enough to call it correctly. It covers what is extracted, how results are shaped, and typical uses.
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%, with both 'input' and 'types' documented, so the schema already does the heavy lifting. The description lists the extractable types, which aligns with the enum, but adds no syntax, default, or filtering guidance 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 begins with a specific verb and resource set: 'Extract all URLs, email addresses, and domain names from text.' This clearly distinguishes the tool from validation siblings like validate_url or validate_email, and from extract_json_from_text.
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?
It names concrete use cases ('content auditing, link checking, and web scraping validation'), giving the agent clear context for when this tool is appropriate. It stops short of explicitly naming exclusions or sibling alternatives, but the intended usage is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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