generate_phone_numberGenerate a synthetic Nigerian phone number for testing or demo data. Optionally select a network: MTN, Glo, Airtel, or 9mobile.
Generates a synthetic Nigerian phone number.
| Name | Required | Description | Default |
|---|---|---|---|
| network | No |
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes |
Changes observed during successful MCP inspections.
v2.0.0Input schema / properties / network / descriptionRemoved value: -"The network of the preferred telco. The available networks are MTN, Glo, Airtel and 9mobile"Input schema / properties / network / enumAdded value: +[
+ "mtn",
+ "glo",
+ "airtel",
+ "9mobile"
+]Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "value": {}
+ },
+ "required": [
+ "value"
+ ],
+ "type": "object"
+}v1.0.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the detail that the number is 'Nigerian' and 'synthetic', which is useful context beyond annotations. However, it does not disclose any other behavioral aspects such as randomness, determinism, or output format, but with annotations covering safety, the description adds some value.
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, efficient sentence with no fluff. It front-loads the core purpose and is appropriately sized for a tool with a single optional parameter. 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?
Given the tool's simplicity and that annotations and an output schema exist, the description is still incomplete. It omits usage guidance and the network parameter semantics, which are critical for correct invocation. An agent cannot fully determine when and how to use this tool based on the description alone.
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 description coverage is 0%, so the description must compensate for explaining the 'network' parameter. It does not mention the parameter at all, leaving the agent to infer from the enum (mtn, glo, airtel, 9mobile) without knowing how it affects the output. The enum is somewhat self-explanatory, but the description fails to connect it to the tool's behavior.
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 states a specific verb ('generates') and a specific resource ('synthetic Nigerian phone number'). It clearly distinguishes from sibling tools that generate people, NINs, vehicle records, etc. However, it doesn't mention the optional network parameter, which is part of the tool's purpose, so it falls short of a 5.
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 provides no guidance on when to use this tool versus alternatives. There is no mention of scenarios (e.g., needing a phone number, or when to use generate_consistent_person for correlated data). No exclusions or comparisons are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.