Call this server's tools from your browser. Nothing to install.
Optional Bearer token (Bearer sys_pro_...) for Pro/Team cloud tools; 3 static auditors run free
Requests go through mcptry because browsers can't call most MCP servers directly. Header values are sent to the server for that request only and are never stored or logged.
Tool list from our last check. Connect live to refresh it.
Ingests standard OpenTelemetry (OTel) trace JSON payloads to detect financial token cost burn traps (> $2.50 threshold), runtime loop deadlocks between sub-agents, and cognitive latency drag hotspots (> 3000ms).
JSON string containing OpenTelemetry trace spans or MCP telemetry log data.
Latency hotspot threshold in milliseconds.
Maximum allowable cost in USD per session before triggering burn trap alert.
Pick your client. Values in capitals, like YOUR_TOKEN, are yours to fill in.
Run in a terminal
claude mcp add --transport http systemores-plugin https://audit.systemores.com/api/mcp
Headers
Authorization (optional, not in the config above): Optional Bearer token (Bearer sys_pro_...) for Pro/Team cloud tools; 3 static auditors run freeIngests standard OpenTelemetry (OTel) trace JSON payloads to detect financial token cost burn traps (> $2.50 threshold), runtime loop deadlocks between sub-agents, and cognitive latency drag hotspots (> 3000ms).
| Argument | Type | Description |
|---|---|---|
| otelTraceJson* | string | JSON string containing OpenTelemetry trace spans or MCP telemetry log data. |
| latencyThresholdMs | integer | Latency hotspot threshold in milliseconds. |
| maxCostThresholdUsd | number | Maximum allowable cost in USD per session before triggering burn trap alert. |
{
"type": "object",
"required": [
"otelTraceJson"
],
"properties": {
"otelTraceJson": {
"type": "string",
"description": "JSON string containing OpenTelemetry trace spans or MCP telemetry log data."
},
"latencyThresholdMs": {
"type": "integer",
"default": 3000,
"description": "Latency hotspot threshold in milliseconds."
},
"maxCostThresholdUsd": {
"type": "number",
"default": 2.5,
"description": "Maximum allowable cost in USD per session before triggering burn trap alert."
}
}
}Verifies the agent's compliance with fair-lending, fair-housing, employment non-discrimination (EEOC/SIOP/ECOA), or child-directed safety (COPPA) standards.
| Argument | Type | Description |
|---|---|---|
| systemPrompt* | string | The underwriting, hiring, or housing agent system prompt to evaluate. Maximum 8,000 characters. |
| complianceDomain* | string | The legal compliance framework to evaluate against (employment, housing, lending, or COPPA child safety). |
{
"type": "object",
"required": [
"systemPrompt",
"complianceDomain"
],
"properties": {
"systemPrompt": {
"type": "string",
"description": "The underwriting, hiring, or housing agent system prompt to evaluate. Maximum 8,000 characters."
},
"complianceDomain": {
"enum": [
"employment_eeoc",
"housing_fair",
"lending_ecoa",
"children_coppa"
],
"type": "string",
"description": "The legal compliance framework to evaluate against (employment, housing, lending, or COPPA child safety)."
}
}
}Statically scans multi-agent execution topologies (DAGs/meshes) using DFS algorithms to detect cyclic delegation loops and privilege escalation paths from public untrusted sub-agents to backend write/admin tools.
| Argument | Type | Description |
|---|---|---|
| maxDepth | integer | Maximum depth to traverse during cycle and privilege path search. |
| graphTopologyJson* | string | JSON-serialized graph topology object containing 'nodes' (id, privilegeLevel) and 'edges' (source, target). |
| strictPrivilegeCheck | boolean | When true, flags any path from 'public'/'user' nodes to 'admin'/'system' nodes as a breach. |
{
"type": "object",
"required": [
"graphTopologyJson"
],
"properties": {
"maxDepth": {
"type": "integer",
"default": 10,
"description": "Maximum depth to traverse during cycle and privilege path search."
},
"graphTopologyJson": {
"type": "string",
"description": "JSON-serialized graph topology object containing 'nodes' (id, privilegeLevel) and 'edges' (source, target)."
},
"strictPrivilegeCheck": {
"type": "boolean",
"default": true,
"description": "When true, flags any path from 'public'/'user' nodes to 'admin'/'system' nodes as a breach."
}
}
}Audits the structural integrity, naming conventions, schema completeness, safety annotations, and injection surface of an AI agent's exposed tool definitions. Identifies malformed schemas, privilege overreach, deceptive safety hints, duplicate names, and description quality gaps. Operates entirely offline — no external API calls required.
| Argument | Type | Description |
|---|---|---|
| strictMode | boolean | When true, applies stricter scoring thresholds — missing optional fields such as parameter descriptions, inputSchema, and MCP tool annotations are penalized. Defaults to false. |
| toolDefinitionsJson* | string | A JSON-serialized array of tool definition objects to audit. Each object should contain at minimum 'name' and 'description' fields. Accepts standard MCP ListToolsResult format. |
{
"type": "object",
"required": [
"toolDefinitionsJson"
],
"properties": {
"strictMode": {
"type": "boolean",
"default": false,
"description": "When true, applies stricter scoring thresholds — missing optional fields such as parameter descriptions, inputSchema, and MCP tool annotations are penalized. Defaults to false."
},
"toolDefinitionsJson": {
"type": "string",
"description": "A JSON-serialized array of tool definition objects to audit. Each object should contain at minimum 'name' and 'description' fields. Accepts standard MCP ListToolsResult format."
}
}
}Performs real-time (~250ms) operational and behavioral turn auditing using Systemores System One. Computes policy compliance score, sycophancy probability, unauthorized tool scope, and returns an inline circuit-breaker action (DISPATCH, INTERCEPT, QUARANTINE, STRIP_TOOL).
| Argument | Type | Description |
|---|---|---|
| userMessage* | string | The prompt or message sent by the user in this interaction turn. |
| systemPrompt* | string | The behavioral blueprint, constraints, or security policies of the agent under audit. |
| agentResponse | string | The proposed text response or output from the agent. |
| proposedToolCall | string | Optional tool invocation or function call proposed by the agent (name and arguments). |
{
"type": "object",
"required": [
"systemPrompt",
"userMessage"
],
"properties": {
"userMessage": {
"type": "string",
"description": "The prompt or message sent by the user in this interaction turn."
},
"systemPrompt": {
"type": "string",
"description": "The behavioral blueprint, constraints, or security policies of the agent under audit."
},
"agentResponse": {
"type": "string",
"description": "The proposed text response or output from the agent."
},
"proposedToolCall": {
"type": "string",
"description": "Optional tool invocation or function call proposed by the agent (name and arguments)."
}
}
}Simulates multiple conversational turns to evaluate agent output consistency and behavioral drift over extended interactions.
| Argument | Type | Description |
|---|---|---|
| turns | integer | The number of conversational turns to simulate (1–100). |
| temperature | number | The LLM temperature parameter for the target agent (0.0–1.5). Higher values increase drift probability. |
| systemPrompt* | string | The target agent's system prompt. Maximum 8,000 characters. |
{
"type": "object",
"required": [
"systemPrompt"
],
"properties": {
"turns": {
"type": "integer",
"default": 10,
"maximum": 100,
"minimum": 1,
"description": "The number of conversational turns to simulate (1–100)."
},
"temperature": {
"type": "number",
"default": 0.7,
"maximum": 1.5,
"minimum": 0,
"description": "The LLM temperature parameter for the target agent (0.0–1.5). Higher values increase drift probability."
},
"systemPrompt": {
"type": "string",
"description": "The target agent's system prompt. Maximum 8,000 characters."
}
}
}Aggregates findings from graph audits, synthetic cohort simulations, chaos tests, and trace analytics into formal compliance report packages for EU AI Act (EU 2024/1689), NIST AI RMF 1.0, EEOC/ECOA, and SOC 2 AI Addendums.
| Argument | Type | Description |
|---|---|---|
| companyName | string | Legal entity or client name for dossier attribution. |
| auditSessionId* | string | Unique identifier for the completed audit session. |
| targetFrameworks | string | Comma-separated list of regulatory frameworks to generate formal dossier sections for: 'eu_ai_act', 'nist_ai_rmf', 'eeoc_ecoa', 'soc2_ai'. Defaults to all if omitted. |
{
"type": "object",
"required": [
"auditSessionId"
],
"properties": {
"companyName": {
"type": "string",
"default": "Enterprise Client",
"description": "Legal entity or client name for dossier attribution."
},
"auditSessionId": {
"type": "string",
"description": "Unique identifier for the completed audit session."
},
"targetFrameworks": {
"type": "string",
"description": "Comma-separated list of regulatory frameworks to generate formal dossier sections for: 'eu_ai_act', 'nist_ai_rmf', 'eeoc_ecoa', 'soc2_ai'. Defaults to all if omitted."
}
}
}Generates statistical cohorts of synthetic user personas across enterprise operational domains with IPIP-NEO-120 30-facet psychometric profiles, demographic diversity, and prompt injection payloads.
| Argument | Type | Description |
|---|---|---|
| domain* | string | The operational domain for persona generation. |
| cohortSize | integer | Number of synthetic personas to generate (1–100). |
| includeAdversarial | boolean | Includes prompt-injection and noisy data submitter personas in the cohort. |
{
"type": "object",
"required": [
"domain"
],
"properties": {
"domain": {
"enum": [
"underwriting",
"customer_support",
"healthcare",
"devops",
"claims"
],
"type": "string",
"description": "The operational domain for persona generation."
},
"cohortSize": {
"type": "integer",
"default": 10,
"maximum": 100,
"minimum": 1,
"description": "Number of synthetic personas to generate (1–100)."
},
"includeAdversarial": {
"type": "boolean",
"default": true,
"description": "Includes prompt-injection and noisy data submitter personas in the cohort."
}
}
}Configures dynamic chaos injection policies for tools, mock APIs, and databases during simulations (HTTP 429 rate limits, 500 errors, timeouts, 401 unauthorized, malformed JSON).
| Argument | Type | Description |
|---|---|---|
| faultType* | string | The specific chaos fault to inject. |
| targetToolName* | string | The target tool name to attach fault injection policy to (or 'all_tools'). |
| failureProbability | number | Probability of fault triggering per invocation (0.01 to 1.0). |
{
"type": "object",
"required": [
"faultType",
"targetToolName"
],
"properties": {
"faultType": {
"enum": [
"rate_limit_429",
"server_error_500",
"timeout",
"unauthorized_401",
"malformed_json"
],
"type": "string",
"description": "The specific chaos fault to inject."
},
"targetToolName": {
"type": "string",
"description": "The target tool name to attach fault injection policy to (or 'all_tools')."
},
"failureProbability": {
"type": "number",
"default": 0.5,
"maximum": 1,
"minimum": 0.01,
"description": "Probability of fault triggering per invocation (0.01 to 1.0)."
}
}
}Measures operational latency, response length, and execution drag parameters to analyze cognitive throughput stability within the Behavioral OS ecosystem.
| Argument | Type | Description |
|---|---|---|
| latencyMs* | number | The end-to-end response latency in milliseconds. Must be >= 1. |
| promptLength* | integer | The character length of the prompt sent to the agent. |
| responseLength* | integer | The character length of the agent's response. |
{
"type": "object",
"required": [
"promptLength",
"responseLength",
"latencyMs"
],
"properties": {
"latencyMs": {
"type": "number",
"minimum": 1,
"description": "The end-to-end response latency in milliseconds. Must be >= 1."
},
"promptLength": {
"type": "integer",
"minimum": 0,
"description": "The character length of the prompt sent to the agent."
},
"responseLength": {
"type": "integer",
"minimum": 0,
"description": "The character length of the agent's response."
}
}
}Exposes the target agent persona to a battery of adversarial prompts (Presses) and evaluates behavioral stability using Murray's Needs-Press diagnostic model.
| Argument | Type | Description |
|---|---|---|
| role* | string | The intended operational function of the agent. |
| systemPrompt* | string | The behavioral blueprint or internal operational instructions of the target agent. Maximum 8,000 characters. |
| pressIntensity | string | The intensity level of adversarial testing. 'high' simulates prompt injection batteries. |
{
"type": "object",
"required": [
"systemPrompt",
"role"
],
"properties": {
"role": {
"enum": [
"customer_support",
"financial_underwriter",
"medical_assistant",
"general_developer"
],
"type": "string",
"description": "The intended operational function of the agent."
},
"systemPrompt": {
"type": "string",
"description": "The behavioral blueprint or internal operational instructions of the target agent. Maximum 8,000 characters."
},
"pressIntensity": {
"enum": [
"low",
"medium",
"high"
],
"type": "string",
"description": "The intensity level of adversarial testing. 'high' simulates prompt injection batteries."
}
}
}Evaluates whether the agent's system prompt establishes the behavioral conditions for safe, reliable tool use — auditing tool selection discipline, argument construction fidelity, and failure resilience when tool returns are ambiguous or erroneous.
| Argument | Type | Description |
|---|---|---|
| toolContext | string | The domain of tool usage being audited. Affects risk weighting. |
| systemPrompt* | string | The agent's system prompt to evaluate for tool use safety. Maximum 8,000 characters. |
{
"type": "object",
"required": [
"systemPrompt"
],
"properties": {
"toolContext": {
"enum": [
"api_calls",
"database",
"filesystem",
"code_execution"
],
"type": "string",
"description": "The domain of tool usage being audited. Affects risk weighting."
},
"systemPrompt": {
"type": "string",
"description": "The agent's system prompt to evaluate for tool use safety. Maximum 8,000 characters."
}
}
}We read every tool's name, description and inputs the way the model will, looking for instructions hidden from you. A clean result means no warning signs, not a guarantee. Scanned Nov 12, 2026.