Random.org MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose focused on generating different types of random data or checking usage. The descriptions specify unique data types (blobs, decimal fractions, Gaussians, integers, sequences, strings, UUIDs) with no overlap in functionality. An agent can easily distinguish between them based on the specific random generation needed.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with 'generate' or 'get' as the verb and a descriptive noun (e.g., generateIntegers, generateUUIDs, getUsage). The naming is uniform across all tools, using camelCase consistently without any deviations or mixed conventions.
Tool Count5/5
With 8 tools, this server is well-scoped for generating various types of random data and checking usage. Each tool serves a specific and necessary function in the domain of random number generation, covering common use cases without being excessive or lacking. The count is appropriate for the server's purpose.
Completeness5/5
The tool set provides complete coverage for the domain of random data generation, including integers, fractions, Gaussians, strings, UUIDs, blobs, sequences, and usage statistics. There are no obvious gaps; it supports a wide range of random generation needs and includes a utility tool for monitoring API usage, ensuring no dead ends for agents.
Average 3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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 provided, the description carries full burden for behavioral disclosure. While 'generate' implies creation, it doesn't specify whether this is a read-only operation, what permissions might be needed, rate limits, or what format the output takes. The 'true random' qualifier adds some context about randomness quality, but overall behavioral traits are minimally described.
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 - a single sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the essential function without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the output looks like (e.g., format, structure), doesn't clarify the relationship with sibling tools, and provides minimal behavioral context despite the complexity of generating random sequences.
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 schema description coverage is 100%, providing comprehensive parameter documentation. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't enhance understanding of parameter meanings or relationships.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate sequences of true random integers' clearly states the verb ('generate') and resource ('sequences of true random integers'), but it doesn't distinguish this tool from its sibling 'generateIntegers' - both appear to generate random integers, making the purpose somewhat vague in context.
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 is provided about when to use this tool versus alternatives like 'generateIntegers' or other sibling tools. The description offers no context about appropriate use cases, 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates 'true random numbers' but doesn't clarify aspects like whether this is a read-only operation, if it has side effects, rate limits, or authentication requirements. For a tool with no annotation coverage, this is a significant gap in transparency.
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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a list of numbers), behavioral traits, or usage context relative to siblings. For a 4-parameter tool with no structured support, the description should provide more comprehensive guidance.
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 schema description coverage is 100%, with all parameters well-documented in the input schema (e.g., 'n' as number of Gaussian numbers with range 1-10,000). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
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 the action ('generate') and resource ('true random numbers from a Gaussian distribution'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'generateDecimalFractions' or 'generateIntegers', which would require mentioning the specific distribution type as a distinguishing feature.
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?
The description provides no guidance on when to use this tool versus alternatives like 'generateDecimalFractions' or 'generateIntegers'. There's no mention of use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
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 provided, the description carries full burden for behavioral disclosure. While 'generate true random integers' implies a read-only operation, it doesn't address important behavioral aspects: whether there are rate limits, authentication requirements, what 'true random' means (source of randomness), whether results are reproducible, or what format the output takes. The description is too minimal for a tool with 5 parameters.
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 a single, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized for a straightforward generation tool and front-loads the essential information. Every word earns its place in conveying the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no output schema, no annotations), the description is insufficiently complete. It doesn't explain the output format, doesn't address the 'true random' claim's implications, and provides no behavioral context. For a generation tool with multiple configuration options and no structured output documentation, the description should do more to help the agent understand what to expect.
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 mentions 'within a specified range' which hints at the min/max parameters, but doesn't add meaningful semantics beyond what the 100% schema coverage already provides. The schema descriptions thoroughly document each parameter's purpose, constraints, defaults, and allowed values. The description provides no additional parameter context, earning the baseline score for high schema coverage.
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 the tool's purpose as 'Generate true random integers within a specified range' - a specific verb ('generate') and resource ('true random integers') with scope ('within a specified range'). However, it doesn't differentiate from sibling tools like 'generateIntegerSequences' which might generate sequential rather than random integers, or explain what makes these integers 'true random' versus pseudo-random.
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?
The description provides no guidance on when to use this tool versus its siblings. There's no mention of alternatives like 'generateIntegerSequences' for sequential generation or 'generateDecimalFractions' for non-integer values. The agent must infer usage from tool names alone, which is insufficient for optimal selection.
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 provided, the description carries the full burden of behavioral disclosure. It mentions 'true random strings' but doesn't explain what 'true random' entails (e.g., cryptographically secure, source of randomness), potential rate limits, error conditions, or output format. This is inadequate for a tool with parameters and no output schema.
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 a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose, making it easy to parse quickly. Every word earns its place by directly conveying the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has four parameters, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like randomness quality, output format (e.g., array of strings), or error handling, leaving significant gaps for the agent to operate effectively.
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% description coverage, clearly documenting all four parameters (n, length, characters, replacement) with constraints and defaults. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.
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 'Generate true random strings' clearly states the verb ('Generate') and resource ('true random strings'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like generateUUIDs or generateBlobs, which also generate random data but of different types.
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?
The description provides no guidance on when to use this tool versus alternatives like generateUUIDs for UUIDs or generateBlobs for binary data. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
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?
No annotations are provided, so the description carries full burden. It states 'Generate true random binary data,' implying a creation operation, but doesn't disclose behavioral traits like whether this requires authentication, has rate limits, or what the output looks like (e.g., format details beyond schema). This is a significant gap for a tool with no annotation coverage.
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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.
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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on output format, behavioral context, or usage guidelines, making it incomplete for optimal agent understanding.
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?
Schema description coverage is 100%, so the schema already documents all parameters (n, size, format) with descriptions, constraints, and defaults. The description adds no additional meaning beyond what the schema provides, such as explaining what 'blobs' are or usage examples, meeting the baseline for high schema coverage.
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 'Generate true random binary data' clearly states the verb ('Generate') and resource ('true random binary data'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like generateStrings or generateUUIDs, which also generate random data but of different types, so it lacks sibling distinction.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where binary data generation is preferred over other random data types, leaving the agent without usage direction.
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?
No annotations are provided, so the description carries the full burden. It mentions 'true random' (implying quality) and the range (0-1), but lacks critical behavioral details: whether this is a read-only operation, if it has rate limits, how randomness is sourced, or what the output format looks like. For a tool with no annotations, this is a significant gap.
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 a single, efficient sentence: 'Generate true random decimal fractions between 0 and 1'. It is front-loaded with the core purpose, has zero wasted words, and appropriately sized for a simple tool. Every part of the sentence adds value.
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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks behavioral context, usage guidance, and output details. The schema handles parameters well, but without annotations or output schema, the description should do more to compensate for these gaps.
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?
Schema description coverage is 100%, so the schema already documents all parameters (n, decimalPlaces, replacement) with descriptions and constraints. The description adds no parameter-specific information beyond what the schema provides, such as explaining the interaction between parameters or default behaviors. Baseline 3 is appropriate when the schema does the heavy lifting.
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 the tool's purpose: 'Generate true random decimal fractions between 0 and 1'. It specifies the verb ('generate'), resource ('true random decimal fractions'), and scope ('between 0 and 1'). However, it doesn't explicitly differentiate from sibling tools like 'generateGaussians' or 'generateIntegers', which likely generate different types of random numbers.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'generateGaussians' (for normal distributions) or 'generateIntegers' (for whole numbers), nor does it specify use cases or prerequisites. The agent must infer usage from the description alone.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get API usage statistics' implies a read-only operation, but it doesn't specify details like authentication requirements, rate limits, data freshness, or response format. This leaves significant gaps in understanding the tool's behavior.
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 'Get API usage statistics' is a single, clear sentence with no wasted words. It's front-loaded and efficiently communicates the core purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficient for a tool that likely returns structured usage data. It doesn't explain what statistics are included, time ranges, or format, leaving the agent with incomplete context for effective use.
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 tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, earning a high baseline score for this dimension.
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 'Get API usage statistics' clearly states the verb ('Get') and resource ('API usage statistics'), making the tool's purpose immediately understandable. However, it doesn't differentiate this tool from its siblings, which are all data generation tools, so it doesn't reach the highest score.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention context, prerequisites, or relationships to sibling tools, leaving the agent with minimal usage direction.
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 provided, the description carries the full burden of behavioral disclosure. It states the tool generates UUIDs but doesn't explain key behaviors such as randomness quality, performance characteristics, or error handling. This leaves significant gaps for a tool with potential complexity in random generation.
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 a single, efficient sentence with zero wasted words, clearly front-loading the core functionality. It's appropriately sized for a simple tool with one parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., format of returned UUIDs, array structure), nor does it cover behavioral aspects like rate limits or error cases, which are important for a generation tool.
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, fully describing the single parameter 'n' with its type, description, and constraints. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
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 specific verb ('Generate') and resource ('true random UUIDs (version 4)'), distinguishing it from sibling tools like generateBlobs or generateStrings by specifying the exact type of data produced. It's precise and unambiguous about what the tool does.
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?
The description provides no guidance on when to use this tool versus alternatives like generateStrings or generateIntegers, nor does it mention any prerequisites or exclusions. It lacks context for tool selection among siblings.
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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