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randomuser

by io.github.pipeworx-io

Generate Users

generate_users
Read-onlyIdempotent

Generate random user profiles with names, addresses, emails, and photos. Filter by nationality (e.g., "US", "GB", "AU") and specify count for multiple profiles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of users to generate (default 1, max 100).
nationalityNoComma-separated nationality codes to filter by (e.g. "us,gb,au"). Supported: AU, BR, CA, CH, DE, DK, ES, FI, FR, GB, IE, IN, IR, MX, NL, NO, NZ, RS, TR, UA, US.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of users returned
usersYesArray of formatted user profiles

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "count": {
+      "description": "Number of users returned",
+      "type": "number"
+    },
+    "users": {
+      "description": "Array of formatted user profiles",
+      "items": {
+        "properties": {
+          "age": {
+            "description": "Age in years",
+            "type": "number"
+          },
+          "cell": {
+            "description": "Mobile/cell number",
+            "type": "string"
+          },
+          "date_of_birth": {
+            "description": "ISO date of birth",
+            "type": "string"
+          },
+          "email": {
+            "description": "Email address",
+            "type": "string"
+          },
+          "gender": {
+            "description": "Gender (male or female)",
+            "type": "string"
+          },
+          "location": {
+            "description": "Address information",
+            "properties": {
+              "city": {
+                "description": "City name",
+                "type": "string"
+              },
+              "country": {
+                "description": "Country name",
+                "type": "string"
+              },
+              "postcode": {
+                "description": "Postal code",
+                "type": "string"
+              },
+              "state": {
+                "description": "State or region",
+                "type": "string"
+              },
+              "street": {
+                "description": "Street address",
+                "type": "string"
+              }
+            },
+            "required": [
+              "street",
+              "city",
+              "state",
+              "country",
+              "postcode"
+            ],
+            "type": "object"
+          },
+          "name": {
+            "description": "Full name with title",
+            "type": "string"
+          },
+          "nationality": {
+            "description": "Nationality code",
+            "type": "string"
+          },
+          "phone": {
+            "description": "Phone number",
+            "type": "string"
+          },
+          "picture": {
+            "description": "URL to medium-sized profile picture",
+            "type": "string"
+          },
+          "username": {
+            "description": "Login username",
+            "type": "string"
+          },
+          "uuid": {
+            "description": "Unique user identifier",
+            "type": "string"
+          }
+        },
+        "required": [
+          "gender",
+          "name",
+          "email",
+          "username",
+          "uuid",
+          "date_of_birth",
+          "age",
+          "phone",
+          "cell",
+          "nationality",
+          "location",
+          "picture"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "count",
+    "users"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "count": 1
+  },
+  {
+    "count": 5,
+    "nationality": "us,gb,au"
+  }
+]
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds behavioral context beyond annotations by stating the profiles are 'random' and that filtering by nationality and specifying count are supported. This complements the readOnlyHint and idempotentHint annotations without contradicting them, though it doesn't detail rate limits or response size.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that conveys the core functionality, parameters, and examples. Every phrase earns its place with no redundant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With annotations, a complete input schema, and an output schema present, the description covers all necessary operational context. It is sufficient for an agent to select and invoke the tool correctly without exceeding its concise length.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with count and nationality already fully documented. The description essentially restates what the schema says (e.g., examples like 'US', 'GB', 'AU'), adding no new parameter semantics beyond the schema's own descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate random user profiles' with specific data fields (names, addresses, emails, photos). It also specifies the key operations (filter by nationality, specify count), making it distinct from siblings like generate_by_gender.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (when random profiles with nationality filters are needed) but provides no explicit exclusions or alternatives. Given the sibling tool generate_by_gender exists, it does not clarify when to choose this over that alternative.

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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TDQS

A3.6/5.0
Disambiguation1/5

Multiple tools have nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly described as identical, and ask_pipeworx_grounded differs only in grounding. generate_users and generate_by_gender overlap, as do ai_visibility_check/scan_competitor_ai_presence and the several polymarket_* tools that all target edge detection and arbitrage. An agent would struggle to select the correct tool.

Naming Consistency2/5

All names use snake_case, but the pattern is inconsistent: some start with verbs (generate_users, resolve_entity, validate_claim), some are nouns (entity_profile, deep_research, recent_changes), and some are compound noun phrases (ai_visibility_check, pipeworx_feedback, polymarket_arbitrage). There is no predictable verb_noun structure across the set.

Tool Count2/5

33 tools is well above the 'too many' threshold, and the set includes many meta-tools, memory helpers, and niche prediction-market tools. While the broad domain might justify some diversity, the count feels bloated and dilutes the server's focus, especially given the server name suggests only random user generation.

Completeness4/5

For the apparent core domain (data lookups, research, prediction market analysis, subscriptions), the tool surface is quite comprehensive: it covers direct queries, grounded answers, deep research, entity profiles, comparisons, claim verification, discovery, trends, memory, and subscription management. Minor gaps exist (e.g., no direct CRUD for user-generated profiles beyond creation), but overall the feature set feels well covered.

Resources