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randomuser

by io.github.pipeworx-io

Generate By Gender

generate_by_gender
Read-onlyIdempotent

Generate random user profiles by gender ("male" or "female"). Returns names, contact info, photos, and demographics. Specify count for multiple profiles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of users to generate (default 1, max 100).
genderYesGender to filter by. One of: male, female.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of users returned
usersYesArray of formatted user profiles
genderYesGender filter applied (male or female)

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"
+    },
+    "gender": {
+      "description": "Gender filter applied (male or female)",
+      "type": "string"
+    },
+    "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",
+    "gender",
+    "users"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "gender": "male"
+  },
+  {
+    "count": 10,
+    "gender": "female"
+  }
+]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With annotations providing readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the safety profile is already clear. The description adds behavioral context by noting the randomness of profiles and the return payload (names, contact info, photos, demographics), which exceeds the annotations' coverage. No contradiction with annotations; 'generate' here is read-only in the sense of producing fictional data without modifying state.

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 two sentences, front-loaded with the main action, and every word adds value. It succinctly covers purpose, return contents, and the multiple-profile option without redundancy.

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?

The tool is simple, has full schema coverage, an output schema present, and rich annotations. The description sufficiently covers the core behavior, and no additional context (like prerequisites or exceptions) is necessary for an agent to select and invoke this tool correctly.

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?

The input schema has 100% coverage for both parameters, including default and max for 'count' and allowed values for 'gender'. The description restates the count use case and gender values but adds no new meaning beyond what the schema already specifies, so baseline 3 is appropriate.

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 uses a specific verb ('Generate') and resource ('random user profiles'), clearly scoped by gender. It distinguishes itself from sibling tool 'generate_users' by explicitly narrowing to gender filtering, making its purpose unambiguous.

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

Usage Guidelines4/5

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

The description clearly indicates usage for generating gender-specific profiles and mentions the count parameter. However, it does not explicitly contrast with sibling tools like 'generate_users' or state when not to use this tool, so it lacks explicit exclusions/alternatives.

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