Random Number MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity: random_choices (weighted selection with replacement), random_sample (unique selection without replacement), random_shuffle (reordering), random_int/float (numeric ranges), and secure variants for cryptographic use. The descriptions clearly differentiate overlapping concepts like choices vs sample.
Naming Consistency5/5
Perfectly consistent snake_case naming throughout with clear verb_noun patterns: all tools start with 'random_' or 'secure_random_' followed by specific operation names (choices, float, int, sample, shuffle, token_hex). The secure variants maintain the same naming convention with a clear prefix.
Tool Count5/5
Seven tools is ideal for a random number generation server - it covers all core random operations (numeric generation, selection, shuffling) plus security variants without being overwhelming. Each tool earns its place with no redundancy or missing essential functionality.
Completeness5/5
The toolset provides complete coverage for random generation needs: numeric generation (int/float), selection (choices/sample), ordering (shuffle), and secure variants for cryptographic applications. There are no obvious gaps - agents can perform any reasonable random operation with this surface.
Average 3.9/5 across 7 of 7 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- Last stable release on July 17, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

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.
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.
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.
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.
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.
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.
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.

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