Extract emails and phone numbers from text.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"text": "<text>"
},
"name": "contacts"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to scan |
Changes observed during successful MCP inspections.
Input schema / properties / textAdded value: +{
+ "description": "Text to scan",
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "text"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full burden and does not meet it. It never states whether the scan is read-only (implied but unconfirmed), what the return shape is (flat list vs. structured objects), whether results are deduplicated or validated, or how it handles text containing neither emails nor phone numbers.
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 with zero filler. Nothing is repeated from the schema or title, and the core capability is stated first.
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?
The tool is intrinsically simple (one string in), so a one-liner is defensible, but with no annotations and no output schema the description is the only source of behavioral information and it says nothing about result format. An agent can call it, but cannot predict what comes back.
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?
Only one parameter exists and its schema description ('Text to scan') covers it fully at 100% coverage, so the baseline of 3 applies. The description's 'from text' phrasing aligns with the schema but adds no new meaning such as size limits, encoding expectations, or whether the whole document is scanned.
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 gives a specific verb and resource: 'Extract emails and phone numbers from text.' An agent immediately knows what it produces. It does not, however, differentiate itself from near-neighbours in the toolset such as 'extract', 'validate_email', 'format_phone', or 'detect_language', leaving ambiguity about which extractor to pick.
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 guidance on when to use this versus alternatives, no prerequisites, and no exclusions. With dozens of extraction/parsing siblings, the agent gets no routing signal at all.
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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