What can you do with this server?

The Random Number MCP server provides both standard pseudorandom and cryptographically secure random generation capabilities, built on Python's standard library: * Generate random integers: Get a random number within a specified inclusive range using random_int * Generate random floats: Get a random decimal number within a range (defaults to 0.0-1.0) using random_float * Choose random items: Select items from a list with optional weights using random_choices * Shuffle items: Return a list with items in random order using random_shuffle * Create secure hex tokens: Generate cryptographically secure random hex tokens using secure_token_hex * Generate secure random integers: Get cryptographically secure random integers below a specified upper bound using secure_random_int The server distinguishes between fast pseudorandom functions for general use cases and more secure functions for security-sensitive applications.

Which integrations are available for this server?

Leverages Python's standard library (random and secrets modules) to provide both standard pseudorandom functions and cryptographically secure random generation capabilities.

How do I use Random Number MCP?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Random Number MCP generate a random integer between 1 and 100" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Random Number MCP

by zazencodes

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Random Number MCP

Essential random number generation utilities from the Python standard library, including pseudorandom and cryptographically secure operations for integers, floats, weighted selections, list shuffling, and secure token generation.

Looking for the agent skill version? random-number-skills implements the same random number generation strategy as an agent skill instead of an MCP server.

Demo Video

https://github.com/user-attachments/assets/303a441a-2b10-47e3-b2a5-c8b51840e362

Related MCP server: Random-Generator

Tools

Tool	Purpose	Python function
`random_int`	Generate random integers	`random.randint()`
`random_float`	Generate random floats	`random.uniform()`
`random_choices`	Choose items from a list (optional weights)	`random.choices()`
`random_shuffle`	Return a new list with items shuffled	`random.sample()`
`random_sample`	Choose k unique items from population	`random.sample()`
`secure_token_hex`	Generate cryptographically secure hex tokens	`secrets.token_hex()`
`secure_random_int`	Generate cryptographically secure integers	`secrets.randbelow()`

Setup

Claude Desktop

Add this to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "random-number": {
      "command": "uvx",
      "args": ["random-number-mcp"]
    }
  }
}

Tool Reference

`random_int`

Generate a random integer between low and high (inclusive).

Parameters:

low (int): Lower bound (inclusive)
high (int): Upper bound (inclusive)

Example:

{
  "name": "random_int",
  "arguments": {
    "low": 1,
    "high": 100
  }
}

`random_float`

Generate a random float between low and high.

Parameters:

low (float, optional): Lower bound (default: 0.0)
high (float, optional): Upper bound (default: 1.0)

Example:

{
  "name": "random_float",
  "arguments": {
    "low": 0.5,
    "high": 2.5
  }
}

`random_choices`

Choose k items from a population with replacement, optionally weighted.

Parameters:

population (list): List of items to choose from
k (int, optional): Number of items to choose (default: 1)
weights (list, optional): Weights for each item (default: equal weights)

Example:

{
  "name": "random_choices",
  "arguments": {
    "population": ["red", "blue", "green", "yellow"],
    "k": 2,
    "weights": [0.4, 0.3, 0.2, 0.1]
  }
}

`random_shuffle`

Return a new list with items in random order.

Parameters:

items (list): List of items to shuffle

Example:

{
  "name": "random_shuffle",
  "arguments": {
    "items": [1, 2, 3, 4, 5]
  }
}

`random_sample`

Choose k unique items from population without replacement.

Parameters:

population (list): List of items to choose from
k (int): Number of items to choose

Example:

{
  "name": "random_sample",
  "arguments": {
    "population": ["a", "b", "c", "d", "e"],
    "k": 2
  }
}

`secure_token_hex`

Generate a cryptographically secure random hex token.

Parameters:

nbytes (int, optional): Number of random bytes (default: 32)

Example:

{
  "name": "secure_token_hex",
  "arguments": {
    "nbytes": 16
  }
}

`secure_random_int`

Generate a cryptographically secure random integer below upper_bound.

Parameters:

upper_bound (int): Upper bound (exclusive)

Example:

{
  "name": "secure_random_int",
  "arguments": {
    "upper_bound": 1000
  }
}

Security Considerations

This package provides both standard pseudorandom functions (suitable for simulations, games, etc.) and cryptographically secure functions (suitable for tokens, keys, etc.):

Standard functions (random_int, random_float, random_choices, random_shuffle): Use Python's random module - fast but not cryptographically secure
Secure functions (secure_token_hex, secure_random_int): Use Python's secrets module - slower but cryptographically secure

Development

Prerequisites

Python 3.10+
uv package manager

Setup

# Clone the repository
git clone https://github.com/example/random-number-mcp
cd random-number-mcp

# Install dependencies
uv sync --dev

# Run tests
uv run pytest

# Run linting
uv run ruff check --fix
uv run ruff format

# Type checking
uv run mypy src/

MCP Client Config

{
  "mcpServers": {
    "random-number-dev": {
      "command": "uv",
      "args": [
        "--directory",
        "<path_to_your_repo>/random-number-mcp",
        "run",
        "random-number-mcp"
      ]
    }
  }
}

Note: Replace <path_to_your_repo>/random-number-mcp with the absolute path to your cloned repository.

Building

# Build package
uv build

# Test installation
uv run --with dist/*.whl random-number-mcp

Release Checklist

Update Version:
- Increment the version number in pyproject.toml, src/random_number_mcp/__init__.py, and server.json.
Update Changelog:
- Add a new entry in CHANGELOG.md for the release.
  - Draft notes with coding agent using git diff context.
  Update the @CHANGELOG.md for the latest release. List all significant changes, bug fixes, and new features. Here's the git diff: [GIT_DIFF]
- Commit along with any other pending changes.
Create GitHub Release:
- Draft a new release on the GitHub UI.
  - Tag release using UI.
- The GitHub workflow will automatically build and publish the package to PyPI.

Testing with MCP Inspector

For exploring and/or developing this server, use the MCP Inspector npm utility:

# Install MCP Inspector
npm install -g @modelcontextprotocol/inspector

# Run local development server with the inspector
npx @modelcontextprotocol/inspector uv run random-number-mcp

# Run PyPI production server with the inspector
npx @modelcontextprotocol/inspector uvx random-number-mcp

MCP Registry

mcp-name: io.github.zazencodes/random-number-mcp

License

MIT License - see LICENSE file for details.

Available Tools

7 tools

random_choicesA

Choose k items from population with replacement, optionally weighted.

Args: population: List of items to choose from k: Number of items to choose (default 1) weights: Optional weights for each item (default None for equal weights)

Returns: List of k chosen items

ParametersJSON Schema

Name	Required	Description	Default
`population`	Yes
`k`	No
`weights`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries full burden for behavioral transparency. It clearly states the sampling method (with replacement) and optional weighting. However, it does not detail edge cases like empty population or weight mismatches, which are typical for such functions.

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 highly concise with two purposeful sentences and a parameter list. Every part contributes meaning without redundancy.

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

Completeness4/5

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

The description covers the core functionality and return type adequately. Given the presence of an output schema (implied by 'Returns list'), it is mostly complete. Minor omissions like error conditions or weight normalization do not detract significantly.

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

Parameters4/5

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

The description adds significant value beyond the input schema by explaining each parameter: population is the list, k is the number of draws, and weights are optional and default to equal weights. This clarifies the purpose of each parameter beyond their types.

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 selects k items from a population with replacement, optionally weighted. This distinguishes it from siblings like random_sample (without replacement) and random_float (single value).

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 explains what the tool does but does not explicitly state when to use it over alternatives. The context of sibling names implies differentiation, but no direct guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

random_floatA

Generate a random float between low and high.

Args: low: Lower bound (default 0.0) high: Upper bound (default 1.0)

Returns: Random float between low and high

ParametersJSON Schema

Name	Required	Description	Default
`low`	No
`high`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.1/5.0

Behavior3/5

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

No annotations provided, so the description carries the full burden. It describes the generation of floats within bounds with defaults, but does not disclose whether the randomness is cryptographically secure (especially relevant given sibling secure_random_int). It also does not specify inclusivity of bounds.

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 extremely concise with a clearly structured format using bullet points. Every sentence adds value and there is no superfluous 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?

For a simple tool with two parameters and an output schema, the description is complete. It covers purpose, parameters, and return value adequately without missing critical information.

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

Parameters4/5

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

Schema coverage is 0%, so the description compensates well by explaining the meaning and defaults of 'low' and 'high'. However, it does not specify whether the bounds are inclusive or exclusive, which could affect usage.

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 the specific verb 'Generate' and clearly identifies the resource as a 'random float between low and high'. It distinguishes from sibling tools like random_int (integer) and random_shuffle (shuffling sequence).

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 usage for generating random floats but does not explicitly state when to use this tool over alternatives such as random_int or secure_random_int. No guidance on exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

random_intB

Generate a random integer between low and high (inclusive).

Args: low: Lower bound (inclusive) high: Upper bound (inclusive)

Returns: Random integer between low and high

ParametersJSON Schema

Name	Required	Description	Default
`low`	Yes
`high`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations, the description must disclose all behavioral traits. It states inclusive bounds and return value, but fails to mention that the random generation is not cryptographically secure (relevant given secure_random_int sibling), potential error behavior for invalid bounds, or distribution uniformity. This is insufficient.

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 extremely concise with a clear structure: one line purpose, then bullet-like Args and Returns. Every sentence adds value with no waste.

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

Completeness3/5

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

Given the simplicity (2 params, output schema exists), the description covers the basic functionality. However, it lacks context about cryptographic security (vs secure_random_int) and fails to mention error handling or constraints, which would be useful for complete agent understanding.

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

Parameters4/5

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

The description adds meaning beyond the schema by explaining 'Lower bound (inclusive)' and 'Upper bound (inclusive)'. However, it does not specify that low must be <= high, which would be helpful. Schema coverage is effectively high because both parameters are explained.

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

Purpose4/5

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

The description clearly states 'Generate a random integer between low and high (inclusive)', which is a specific verb-resource. It distinguishes itself from sibling tools like random_float (generates floats) and random_choices, but does not explicitly differentiate from secure_random_int, which could be confusing.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. There is no mention of non-cryptographic nature relative to secure_random_int, nor any prerequisites or constraints (e.g., low must be <= high). Usage is only implied by the name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

random_sampleA

Choose k unique items from population without replacement.

Args: population: List of items to choose from k: Number of items to choose

Returns: List of k unique chosen items

ParametersJSON Schema

Name	Required	Description	Default
`population`	Yes
`k`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It explains the output (list of k unique items) and the without-replacement behavior. It does not mention error conditions like k > population size, but for a simple random sample this is acceptable.

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 concise with 5 lines, structured cleanly with Args and Returns sections, no unnecessary words.

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

Completeness4/5

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

Given the tool's simplicity and the presence of an output schema (though not shown), the description is largely complete. It covers purpose, parameters, and return value. A slight gap is not mentioning the error case when k > population size.

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

Parameters4/5

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

The description adds meaning to both parameters beyond the bare schema: 'population: List of items to choose from' and 'k: Number of items to choose'. This compensates for the 0% schema coverage. However, it could be more explicit about constraints (e.g., k must be <= length of population).

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 verb 'choose' and the resource 'k unique items from population without replacement', which is specific and distinguishes from the sibling 'random_choices' which likely does with replacement.

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 (when without replacement) but does not explicitly state when not to use or compare to alternatives like 'random_choices' for with-replacement sampling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

random_shuffleA

Return a new list with items in random order.

Args: items: List of items to shuffle

Returns: New list with items in random order

ParametersJSON Schema

Name	Required	Description	Default
`items`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.6/5.0

Behavior3/5

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

The description discloses that the operation returns a new list (non-destructive) and uses random order, but lacks details about randomness quality (e.g., cryptographic vs. pseudo-random) or performance implications. With no annotations, the description provides basic transparency but could be more informative.

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 extremely concise: two sentences plus standard args/returns docstring. Every sentence is necessary, with no redundant or irrelevant information. Well-structured for quick consumption.

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

Completeness4/5

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

Given the tool's simplicity (one parameter, straightforward purpose), the description covers the essential behavior. It states the return type and action. The presence of an output schema (not shown) likely completes the contract, so the description is adequate.

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 description adds marginal value beyond the input schema by labeling 'items' as a 'List of items to shuffle.' Schema coverage is 0%, but the single-parameter simplicity and clear docstring compensate somewhat. However, no additional constraints or example formats are provided.

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 'Return a new list with items in random order,' specifying the verb ('return'), resource ('list'), and operation ('random order'). It distinguishes from siblings like random_choices and random_sample by focusing on shuffling the entire list, not sampling.

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

Usage Guidelines2/5

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

No usage guidance is provided. The description does not indicate when to use this tool over alternatives like random_choices or random_sample, nor does it mention any prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

secure_random_intA

Generate a secure random integer below upper_bound.

Args: upper_bound: Upper bound (exclusive)

Returns: Random integer in range [0, upper_bound)

ParametersJSON Schema

Name	Required	Description	Default
`upper_bound`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description must disclose behavior. It states 'secure' (suggesting cryptographic randomness) and defines the output range. However, it does not explain security implications or constraints (e.g., upper_bound must be positive). It is adequate but not comprehensive.

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?

Two sentences with clear purpose and parameter description. Front-loaded with the main action. No wasted words.

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

Completeness4/5

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

For a simple tool with an output schema, the description covers its own behavior and parameters well. However, it lacks guidance on when to use this tool over siblings, slightly reducing completeness.

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

Parameters5/5

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

The schema has 0% parameter coverage, so the description fully compensates by explaining 'upper_bound: Upper bound (exclusive)', adding critical meaning beyond the schema's raw type.

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 verb 'Generate' and the resource 'secure random integer', with the constraint 'below upper_bound'. This precisely distinguishes it from sibling tools like random_int, which may not emphasize security.

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

Usage Guidelines2/5

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

No guidance on when to use this tool over alternatives like random_int or random_float. The description does not provide context or exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

secure_token_hexA

Generate a secure random hex token.

Args: nbytes: Number of random bytes to generate (default 32)

Returns: Hex string containing 2*nbytes characters

ParametersJSON Schema

Name	Required	Description	Default
`nbytes`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.8/5.0

Behavior2/5

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

With no annotations, the description carries the full burden. It mentions 'secure random' implying cryptographic security, but does not disclose potential behavioral traits like performance, blocking, or error conditions for invalid input.

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 concise with clear Args and Returns sections. Every sentence is informative, no redundancy, and efficiently structured for a single-parameter tool.

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

Completeness4/5

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

Given the tool's simplicity, the description covers input parameter and output format well via the docstring. Missing edge-case constraints (e.g., nbytes=0) but still sufficient for basic use. Output schema exists, so return details are adequately handled.

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

Parameters4/5

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

Schema coverage is 0%, so the description compensates well by explaining the nbytes parameter: number of random bytes, default 32. It adds meaning beyond the schema's type and default, though could clarify that nbytes must be positive.

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 generates a secure random hex token, with specific verb 'generate' and resource 'secure random hex token'. It differentiates from sibling tools like random_choices, random_int, etc., which serve different purposes.

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?

No explicit when-to-use or when-not-to-use guidance is provided. The name and description imply it is for secure hex tokens, distinguishing it from siblings, but there is no direct comparison or exclusion statement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

7 tool updatesv0.1.0May 2, 2026

Changedrandom_choices8 fields changed

removedInput schema / properties / k / title
```
Removed value: -"K"
```

addedInput schema / properties / population / items / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  },
+  {
+    "type": "boolean"
+  }
+]

removedInput schema / properties / population / title
```
Removed value: -"Population"
```
removedInput schema / properties / weights / title
```
Removed value: -"Weights"
```

addedOutput schema / description

Added value: +"Generic wrapper for non-object return types."

addedOutput schema / properties / result / items / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  },
+  {
+    "type": "boolean"
+  }
+]

removedOutput schema / properties / result / title
```
Removed value: -"Result"
```
removedOutput schema / title
```
Removed value: -"_WrappedResult"
```

Changedrandom_float5 fields changed
- removedInput schema / properties / high / title
```
Removed value: -"High"
```
- removedInput schema / properties / low / title
```
Removed value: -"Low"
```
- addedOutput schema / description
```
Added value: +"Generic wrapper for non-object return types."
```
- removedOutput schema / properties / result / title
```
Removed value: -"Result"
```
- removedOutput schema / title
```
Removed value: -"_WrappedResult"
```
Changedrandom_int5 fields changed
- removedInput schema / properties / high / title
```
Removed value: -"High"
```
- removedInput schema / properties / low / title
```
Removed value: -"Low"
```
- addedOutput schema / description
```
Added value: +"Generic wrapper for non-object return types."
```
- removedOutput schema / properties / result / title
```
Removed value: -"Result"
```
- removedOutput schema / title
```
Removed value: -"_WrappedResult"
```

Changedrandom_sample7 fields changed

removedInput schema / properties / k / title
```
Removed value: -"K"
```

addedInput schema / properties / population / items / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  },
+  {
+    "type": "boolean"
+  }
+]

removedInput schema / properties / population / title
```
Removed value: -"Population"
```

addedOutput schema / description

Added value: +"Generic wrapper for non-object return types."

addedOutput schema / properties / result / items / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  },
+  {
+    "type": "boolean"
+  }
+]

removedOutput schema / properties / result / title
```
Removed value: -"Result"
```
removedOutput schema / title
```
Removed value: -"_WrappedResult"
```

Changedrandom_shuffle6 fields changed

addedInput schema / properties / items / items / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  },
+  {
+    "type": "boolean"
+  }
+]

removedInput schema / properties / items / title
```
Removed value: -"Items"
```

addedOutput schema / description

Added value: +"Generic wrapper for non-object return types."

addedOutput schema / properties / result / items / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  },
+  {
+    "type": "boolean"
+  }
+]

removedOutput schema / properties / result / title
```
Removed value: -"Result"
```
removedOutput schema / title
```
Removed value: -"_WrappedResult"
```

Changedsecure_random_int4 fields changed
- removedInput schema / properties / upper_bound / title
```
Removed value: -"Upper Bound"
```
- addedOutput schema / description
```
Added value: +"Generic wrapper for non-object return types."
```
- removedOutput schema / properties / result / title
```
Removed value: -"Result"
```
- removedOutput schema / title
```
Removed value: -"_WrappedResult"
```
Changedsecure_token_hex4 fields changed
- removedInput schema / properties / nbytes / title
```
Removed value: -"Nbytes"
```
- addedOutput schema / description
```
Added value: +"Generic wrapper for non-object return types."
```
- removedOutput schema / properties / result / title
```
Removed value: -"Result"
```
- removedOutput schema / title
```
Removed value: -"_WrappedResult"
```

7 tool updatesv1.0.0Aug 15, 2025
- First observedrandom_choices
- First observedrandom_float
- First observedrandom_int
- First observedrandom_sample
- First observedrandom_shuffle
- First observedsecure_random_int
- First observedsecure_token_hex

Deploy Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.8/5.0

Scored 2026-09-10 01:28 across 7 tools

Disambiguation4/5

Most tools have clearly distinct purposes: integer, float, secure integer, token, sample, shuffle, and choices. There is mild overlap between random_int and secure_random_int, but the inclusive/exclusive bounds and cryptographic distinction in descriptions help disambiguate them.

Naming Consistency4/5

Naming follows a mostly consistent snake_case pattern with random_* for general operations and secure_* for cryptographic variants. The only minor inconsistency is secure_random_int breaking the random_* prefix pattern, but the naming remains predictable and readable.

Tool Count5/5

Seven tools is well-scoped for a random number utility server. Each tool addresses a distinct common random-generation need without unnecessary redundancy or bloat.

Completeness4/5

The server covers the core random number domain well: integers, floats, secure randomness, sampling, shuffling, and weighted choices. Minor gaps such as random boolean generation or non-uniform distributions exist but are not essential for the stated purpose.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Reddit Discussion

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Random Number MCP

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random_int

random_float

random_choices

random_shuffle

random_sample

secure_token_hex

secure_random_int

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`random_int`

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