Random Value MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_random_numberB | Generate a random integer within a specified range |
| generate_random_stringC | Generate a random string of specified length using alphanumeric characters |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one generates random numbers within a range, while the other generates random strings of a specified length. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the desired output type.
Both tools follow a consistent verb_noun naming pattern with 'generate_random_' as the prefix, followed by the specific output type ('number' or 'string'). This uniformity makes the tool set predictable and easy to understand.
With only two tools, the server feels thin for a 'Random Value MCP Server' that might be expected to handle more varied random generation tasks (e.g., booleans, floats, UUIDs, or selections from lists). The scope is minimal, limiting its utility in broader contexts.
The tool set is severely incomplete for a random value generation domain. It lacks common operations like generating random booleans, floats, UUIDs, or picking random items from lists, which are typical needs in such applications. This will likely cause agent failures when more diverse random values are required.