random-mcp
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 | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| random_numberA | Return one uniformly distributed random float in [0, 1). |
| random_intervalA | Sample uniformly between bounds; kind='integer' includes both endpoints. Float sampling can include the upper bound through rounding. Equal bounds return that value. Integer bounds must be whole numbers. |
| random_normalA | Return one normal sample; defaults to mean 0 and standard deviation 1. Parameters must be finite and standard_deviation must be positive. |
| roll_diceA | Roll NdM dice or sums, such as 2d10 or 1d10 + 2d100 + 5d4. Return count, sides, subtotal per term and a grand total. Full detail also includes every roll; compact omits rolls. Auto uses compact above 100 dice. Only addition is supported; no modifiers, subtraction, or parentheses. Limits: 10,000 total dice, 100 terms, 1,000,000 sides per die, and 10,000 expression 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 4 tools
Each tool targets a distinct distribution or use case: uniform float, bounded interval, normal, and dice notation. random_number and random_interval overlap conceptually since random_number is a special case of uniform sampling, but the descriptions clarify the intended difference.
Three tools follow a consistent random_noun pattern, while roll_dice breaks the pattern with a verb_noun style. The naming is still predictable and readable, but the one deviation keeps it from a perfect score.
Four tools is a well-scoped size for a random number generation server. Each tool serves a common random sampling need without unnecessary bloat.
The set covers standard uniform, bounded interval, normal, and dice-style generation, which covers most common use cases. Minor gaps like seeding or random choice are absent but not critical for the apparent purpose.