CyberSim Pro MCP Server

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation4/5
Most tools have distinct purposes (e.g., analyze_network vs forensics_analysis vs investigate_incident), but generate_report and generate_validation_report could be confused at a glance, as could forensics_analysis and investigate_incident. Descriptions clarify boundaries, but a couple tools overlap in scope.
Naming Consistency4/5
The vast majority follow a verb_noun snake_case pattern (analyze_network, create_scenario, simulate_attack), but forensics_analysis deviates by placing the noun first and using 'analysis' instead of a verb. This is a minor inconsistency in an otherwise predictable convention.
Tool Count5/5
With 12 tools, the server is well-scoped for a cybersecurity simulation platform. Each tool serves a distinct function in the workflow, and the count is within the ideal 3-15 range, making the surface manageable without being sparse.
Completeness4/5
The tool set covers the core lifecycle: scenario creation, attack simulation, incident investigation, forensics, reporting, and integration with risk registers. Minor gaps exist, such as lacking a tool to list or update scenarios, but the available tools are sufficient for typical training and analysis workflows.
Average 3.1/5 across 12 of 12 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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}
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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 the full burden of disclosing behaviors. It only says 'perform... analysis' with no indication of side effects, read-only nature, data access, or potential impact on the system. This is insufficient for a tool that could be intrusive or require permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one sentence), but that brevity comes at the cost of substance. It is under-specified rather than appropriately concise, providing no additional context beyond the tool name.
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?
Without an output schema, annotations, or any behavioral details, the description is incomplete for a complex task like digital forensics. It doesn't explain what results are returned, whether it's a one-time analysis or ongoing, or any operational nuances.
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 describes all three parameters with clear meaning (e.g., artifact_type enum, system_id type), and schema coverage is 100%. The description adds nothing beyond that, but the schema itself is sufficient for parameter understanding.
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 gives a clear verb ('perform') and resource ('digital forensics analysis on system artifacts'), but it's vague and doesn't distinguish from sibling tools like 'investigate_incident' or 'analyze_network'. It conveys the general function but lacks specificity about scope.
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 vs alternatives. There is no mention of prerequisites, typical scenarios, or exclusions, so an agent would not know how to choose it over the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavioral traits. It fails to mention whether the analysis is read-only, long-running, requires special permissions, or produces any output — leaving the agent uninformed about side effects or execution expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no redundancy or filler. It is front-loaded with the main action, but its brevity limits structural organization; despite this, it earns its place by conveying the core purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is severely under-specified for a tool with no output schema and no annotations. It does not explain what the analysis returns, how results are presented, any time/scope implications, or how it fits into the broader workflow, making it inadequate for confident invocation.
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%, with all three parameters (focus, duration, network_segment) having meaningful descriptions. The tool description adds no extra information about the parameters, but the schema already provides clear semantics, so the baseline of 3 applies.
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 primary action ('Analyze network traffic') and its goal ('identify potential security issues'), using a specific verb and resource. However, it does not explicitly differentiate it from sibling tools like forensics_analysis or investigate_incident, which could also analyze network-related data.
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. There is no mention of appropriate scenarios, prerequisites, or exclusions, leaving the agent without clear decision-making support for tool 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?
Since annotations are absent, the description must carry the full burden of behavioral disclosure. It only says 'Create' without detailing side effects, persistence, permissions, or what happens to existing scenarios. No additional behavioral traits are revealed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no fluff. However, it lacks any structural elements like bullets or sections that could make the information more scannable. For the bare purpose, it is efficient but not richly structured.
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 6 parameters, no output schema, and no annotations, the description is grossly insufficient. It does not explain what the tool returns, how it manages state, or any operational context. A user/agent needs far more detail to use this 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?
Schema description coverage is 100%, so the baseline is 3. The description's 'customizable parameters' adds no semantic value beyond what the schema already provides. It does not clarify parameter relationships or usage patterns.
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 'Create a cybersecurity training scenario' with a specific verb and resource. It is unambiguous about the tool's core function, though it does not explicitly differentiate from sibling tools like simulate_attack. The name itself strongly implies its unique role.
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?
There is no guidance on when to use this tool versus alternatives. No mention of prerequisites, typical use cases, or exclusions. Sibling tools like simulate_attack or investigate_incident are not referenced, leaving the agent without context for 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?
No annotations are provided, so the description must disclose behavioral traits. It only says 'generate', without explaining whether it writes data, requires specific input data, has side effects, or what the output format is. This is insufficient for an agent to understand the tool's impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no unnecessary words. It is front-loaded with the core action, but its brevity comes at the cost of missing useful context. Still, it is efficiently written.
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?
With four parameters, no annotations, and no output schema, the description leaves significant gaps. It does not explain the report types, expected inputs, output structure, or when this tool is appropriate, making it incomplete for an agent to use confidently.
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 descriptions cover 100% of parameters, so the baseline is 3. The tool description adds no parameter-specific information beyond what the schema already provides, which is acceptable given the 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 generates security assessment or incident reports, with a specific verb and resource. However, it does not explicitly distinguish itself from the sibling tool 'generate_validation_report', and the term 'comprehensive' is vague, leaving some ambiguity about the exact scope.
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 does not mention any prerequisites, exclusions, or scenarios where this tool is preferred, such as when to pick generate_report over generate_validation_report or investigate_incident.
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 does not explain side effects, permission requirements, whether replay mutates the simulation, or what output is produced. The word 'replay' hints at a non-destructive activity, but this is not explicit.
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, well-structured sentence with no wasted words. It front-loads the action and data type while also conveying the purpose, making it appropriately concise.
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?
The tool has six parameters, nested objects, no output schema, and no annotations, so a one-sentence description is insufficient for operational context. It does not explain how the approval_token/operator fields are used or what the replay result looks like, leaving significant gaps for an agent invoking this 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?
Schema description coverage is high (83%), and the schema already describes telemetry, simulation_id, scenario_id, and telemetry_base64. The description adds no parameter-level meaning, but given the high schema coverage, the baseline of 3 is appropriate.
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 uses a specific verb ('replay') and resource ('lab telemetry') and clearly states the goal ('identify coverage gaps'). It implies the tool operates on an existing simulation, which distinguishes it from creation/simulation tools, though it does not explicitly name a sibling alternative.
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 siblings like simulate_attack or generate_validation_report, nor does it mention prerequisites or exclusions. The purpose implies a use case, but there is no actionable selection guidance.
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?
There are no annotations, so the description carries full responsibility for disclosing behavior. It mentions "simulate" which hints at non-real effects, but it fails to clarify side effects, safety, or the explicit approval requirement hinted by the approval_token parameter. The phrase "realistic attack vectors" makes the level of danger ambiguous.
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, focused sentence that immediately communicates the core purpose without any wasted words. It is concise and easy to parse.
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?
Despite having moderate complexity (5 params, nested objects, no output schema), the description lacks operational context. It does not explain the simulation's execution model, potential side effects, approval requirements, or expected output. The parameter schema covers fields, but the overall tool behavior is under-specified for a potentially high-impact simulation 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?
Schema coverage is 100%, meaning all 5 parameters have descriptions in the schema. The tool description adds no additional parameter semantics—it doesn't explain how parameters interact or provide extra context beyond the schema. The baseline of 3 is appropriate.
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 function with a specific verb-resource pair: "Simulate a cyberattack" and adds meaningful qualifiers ("realistic attack vectors and TTPs"). However, it does not explicitly differentiate this tool from siblings like create_scenario, so it falls short of the top 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?
No guidance is provided about when to use this tool vs. alternatives. The description doesn't mention use cases, prerequisites, or conditions that would favor simulate_attack over create_scenario or analyze_network.
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 must carry the full burden of behavioral disclosure. It only says 'generate payloads', which hints at a non-destructive preparation step, but it does not clarify whether the tool actually updates external systems, requires authentication, or has side effects. This is a significant gap for a sync/update tool.
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, focused sentence with no redundant information. It efficiently communicates the core purpose and target systems, making it appropriately concise.
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?
With no annotations, no output schema, and a moderate schema, the description must provide more context than it does. It fails to explain the payload structure, return values, side effects, or prerequisites, which is insufficient for an agent to correctly invoke a multi-system synchronization tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema documents 60% of parameters (system and incident_id), but the description adds no parameter-level meaning beyond that. The remaining parameters (owner, due_date, priority) are only typed as strings with no description in either the schema or the tool description, leaving their purpose and format ambiguous.
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 identifies a specific action (generating payloads) and a clear resource (enterprise risk registers) with named target systems (ServiceNow, Archer, OneTrust), distinguishing it from sibling security tools. However, the verb 'generate payloads' is indirect compared to the tool name 'sync', leaving slight ambiguity about whether it directly updates or prepares data.
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 guidance is provided, but the purpose is reasonably clear from context: it is for updating risk registers after incidents. There are no exclusions or alternatives mentioned, so the usage context is implied rather than explicit.
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 must disclose behavior, but it only mentions 'timeline reconstruction' without explaining side effects, data access, security requirements, or output format. This leaves substantial ambiguity for an agent.
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, front-loaded sentence that conveys the core action and outcome with no unnecessary words. It is concise and well-structured.
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 and absence of annotations or output schema, the description is too sparse. It does not explain what 'investigation' entails, what the response will contain, or how 'timeline reconstruction' is delivered, leaving critical gaps for an agent.
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 a baseline of 3 applies. The description itself adds no extra meaning beyond the schema, and the enum values (initial/full/deep_dive) are not elaborated.
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 identifies the tool's purpose with a specific verb ('conduct') and resource ('incident response investigation'), and adds a distinctive feature ('timeline reconstruction') that helps distinguish it from siblings like forensics_analysis.
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 indication of when to use this tool versus alternatives such as forensics_analysis or analyze_network. It lacks any contextual cues about scenarios that warrant investigation or prerequisites.
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 of behavioral disclosure. It implies a read-only operation ('Summarise') and limits scope to 'historical' data, but it does not disclose return format, pagination, potential rate limits, or any other behavioral traits. The description is too minimal to provide meaningful 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, concise sentence that states the purpose without any filler words. Every word contributes to understanding the tool's function, making it appropriately sized and front-loaded.
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 that the tool has no parameters and no output schema, the description is reasonably complete for a simple list/summary tool. It conveys the core function and scope (historical exercise metrics and readiness trends). However, it could be improved by mentioning what the output looks like or any limitations, but for its simplicity, it covers the essentials.
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 zero parameters, so the schema already covers everything. The description does not need to add parameter details, and the baseline for no parameters is 4. The description's mention of 'historical' and 'readiness trends' gives some context but does not conflict with the empty schema.
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 uses a specific verb ('Summarise') and identifies clear resources ('historical exercise metrics and readiness trends'). It distinguishes itself from sibling tools like analyze_network or simulate_attack by focusing on metrics summarization rather than analysis or simulation. However, it lacks the precision of examples like 'List ALL calls in date range' because it does not specify the exact scope or output format.
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 on when to use this tool versus alternatives. The description merely states what it does, without any 'when to use' or 'when not to use' instructions, nor does it mention alternative tools for different scenarios.
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 for disclosing behavioral traits. It only states the action (stop) without explaining consequences such as irreversibility, state changes, permission requirements, or audit implications. The schema hints at governance (approval_token, reason) but the description does not surface these.
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, front-loaded sentence with no filler. Every word contributes to conveying the core purpose and context, making it highly concise.
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?
The description is too sparse for a tool with four parameters, a nested object, no annotations, and no output schema. It fails to explain key context such as the default behavior when simulation_id is omitted, governance requirements, or what happens to the simulation's state after stopping.
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% and includes detailed descriptions for all four parameters. The tool description adds no additional meaning beyond the schema, so the baseline score of 3 is appropriate.
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's function: 'Manually stop one or more active simulations.' It uses a specific verb (stop) and resource (simulations), and differentiates itself from siblings like simulate_attack by indicating the inverse action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for safety or compliance' provides contextual guidance on when to use the tool. It does not explicitly mention alternatives or exclusions, but the context is clear enough that an agent can infer this is the appropriate tool for halting simulations.
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 must carry the full burden of behavioral disclosure. It states the action (produce) but does not disclose side effects, output format or contents, permissions, or any limitations. For a tool with no annotation safety hints, this leaves significant gaps in what the agent can expect.
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, front-loaded sentence with no wasted words. It efficiently conveys the purpose, audience, and scope, making it easy for an agent to parse and act on.
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?
With no output schema and no annotations, the description is the only source of context. It is minimal and does not explain what a 'validation digest' contains, how it is delivered, or how recent the activity is. While the low complexity (no parameters) helps, the vagueness of the output leaves the description only minimally adequate.
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 zero parameters, so the input schema already fully covers the interface. According to the rubric, 0 parameters yields a baseline score of 4. The description adds no parameter details, but none are needed.
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 'Produce' with a clear resource, 'auditor-facing validation digest', and scopes it to 'recent CyberSim activity'. This clearly distinguishes it from the sibling 'generate_report' and other tools, which are more generic or focus on different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'auditor-facing' provides a clear context for when to use this tool (for audit validation), but it does not explicitly compare to alternatives or state when not to use it. This is more than implied usage but lacks explicit exclusions, aligning with a clear context without delimiting guidance.
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 provided, the description carries the full burden. It states the action and source but does not explicitly disclose that the operation is read-only or whether it has side effects. 'Export' implies a non-mutating operation, but this is not stated, and the output format is not mentioned.
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, front-loaded sentence that is free of unnecessary words or technical jargon. It efficiently communicates the tool's purpose without any waste.
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 parameterless export tool, the description is nearly complete. It explains what is exported and the source, which is sufficient for an agent to decide when to call it. However, it does not specify the output format or whether a prior analysis is required, leaving a slight gap in expected behavior.
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 zero parameters, and the baseline for this case is 4. The description adds meaning by clarifying what is exported (compensating controls) and from where (CyberSim analyses), which is useful context beyond the empty schema.
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 a specific verb 'Export' and specifies the resource 'recommended compensating controls' along with their source 'derived from CyberSim analyses'. This clearly distinguishes it from siblings like generate_report or sync_risk_register, which have 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?
The description implies usage in the context of CyberSim analyses but does not explicitly state when to use this tool versus alternatives. It lacks a clear when-to-use or when-not-to-use statement, leaving the agent to infer based on the name and 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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