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Uses a middleware pattern that sits between the client and tool handlers, evaluating each call against a policy engine before delegation to the actual tool implementation.","intents":["I need to prevent certain tool calls from executing based on predefined rules without modifying the underlying tools","I want to enforce organizational policies on what agents can do before they actually do it","I need to block dangerous operations (file deletion, external API calls) at the MCP protocol level"],"best_for":["teams deploying AI agents in production environments with strict governance requirements","enterprises needing compliance-driven tool access control without rewriting agent code","developers building multi-tenant AI systems where different users have different tool permissions"],"limitations":["Blocking is deterministic only — cannot handle probabilistic or context-dependent policies without custom rule logic","No built-in support for dynamic policy updates without server restart unless custom persistence layer is added","Performance depends on policy rule complexity — deeply nested conditions can add latency to every tool call"],"requires":["Node.js 16+ (MCP server runtime)","MCP-compatible client (Claude Desktop, custom MCP client, or Anthropic SDK with MCP support)","Policy configuration file or API for defining blocking rules"],"input_types":["MCP tool call messages (JSON-RPC format)","tool name (string)","tool arguments (JSON object)","execution context metadata"],"output_types":["allow/block/hold decision (enum)","decision rationale (string)","modified tool call (optional, for argument sanitization)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-promptspeak-mcp-server__cap_1","uri":"capability://safety.moderation.human.in.the.loop.approval.holds.for.flagged.tool.calls","name":"human-in-the-loop approval holds for flagged tool calls","description":"Pauses execution of flagged tool calls and routes them to a human approval queue, blocking agent execution until explicit human authorization is received. Implements a hold state in the MCP message flow where the server returns a pending response, maintains call state, and waits for external approval signals before proceeding or rejecting the call.","intents":["I want to require human approval for high-risk operations like database modifications or external API calls","I need to audit sensitive tool calls before they execute in production","I want to give users the ability to review and approve agent actions in real-time without stopping the entire agent"],"best_for":["regulated industries (finance, healthcare) requiring audit trails and human oversight of agent actions","teams using agents for critical business processes where mistakes are costly","organizations building customer-facing AI systems that need transparency and control"],"limitations":["Adds latency to agent execution — human approval time is unpredictable and can block agent progress indefinitely","Requires external approval infrastructure (queue, notification system, approval UI) — not included in the server itself","No built-in timeout mechanism — held calls can remain pending indefinitely if approval is never received","Stateful design requires persistence layer to survive server restarts without losing pending approvals"],"requires":["Node.js 16+","External approval backend or webhook endpoint to receive approval notifications","Mechanism to notify humans of pending approvals (email, Slack, custom dashboard)","Client capable of handling async approval responses (not all MCP clients support this pattern)"],"input_types":["MCP tool call with hold flag or risk classification","approval decision (approve/reject) from external system","approval metadata (approver ID, timestamp, reason)"],"output_types":["hold status with pending ID (string)","approval decision response (allow/reject)","audit log entry with approver information"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-promptspeak-mcp-server__cap_2","uri":"capability://safety.moderation.behavioral.drift.detection.for.agent.tool.usage.patterns","name":"behavioral drift detection for agent tool usage patterns","description":"Monitors tool call patterns over time and detects statistical deviations from baseline behavior, flagging unusual sequences, frequency spikes, or novel tool combinations that may indicate agent malfunction or drift. Uses statistical analysis of call history to establish baselines and identify anomalies without requiring explicit rule definition.","intents":["I want to detect when an agent starts behaving differently than its normal patterns without writing specific rules","I need to catch agent degradation or prompt injection attacks by identifying unusual tool call sequences","I want to monitor for gradual behavioral drift that might indicate model updates or training data changes affecting agent behavior"],"best_for":["teams running long-lived agents in production that need continuous behavioral monitoring","organizations concerned about prompt injection or adversarial attacks on agents","developers building self-healing or self-monitoring agent systems"],"limitations":["Requires historical baseline data — new agents have no baseline and cannot detect drift until sufficient history is collected","Statistical detection produces false positives and false negatives — tuning sensitivity requires domain knowledge","Cannot distinguish between legitimate behavior changes and actual drift without additional context","Computationally expensive for high-frequency tool calls — may require sampling or windowing strategies"],"requires":["Node.js 16+","Time-series data storage for tool call history (in-memory, database, or external service)","Minimum baseline period (typically 100+ calls) before drift detection is reliable","Configuration for statistical thresholds (z-score, percentile, or custom anomaly detection algorithm)"],"input_types":["tool call history (sequence of tool names, arguments, timestamps)","baseline period configuration (time window or call count)","anomaly detection parameters (sensitivity, algorithm choice)"],"output_types":["drift detection alert (boolean)","anomaly score (numeric, 0-1 or z-score)","explanation of detected drift (string with pattern description)","baseline statistics for comparison"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-promptspeak-mcp-server__cap_3","uri":"capability://tool.use.integration.mcp.protocol.level.tool.call.validation.and.schema.enforcement","name":"mcp protocol-level tool call validation and schema enforcement","description":"Validates incoming tool calls against declared MCP tool schemas, enforcing argument types, required fields, and value constraints before execution. Implements schema validation at the protocol layer by parsing tool definitions from the MCP server's resource list and applying JSON Schema validation to each call.","intents":["I want to ensure tool calls conform to their declared schemas before they reach the tool implementation","I need to catch malformed or invalid tool calls early to prevent runtime errors in tools","I want to enforce stricter validation than the tool itself provides without modifying tool code"],"best_for":["teams with heterogeneous tool implementations that may have inconsistent validation","developers building robust agent systems where invalid calls should fail fast","organizations needing audit-compliant validation of all tool inputs"],"limitations":["Validation is static — cannot validate against dynamic constraints (e.g., 'user must own this resource')","Schema enforcement depends on accurate tool schema declarations — incomplete or incorrect schemas reduce effectiveness","No support for custom validation logic beyond JSON Schema — complex business rules require additional policy layer"],"requires":["Node.js 16+","MCP tools with properly declared schemas in their resource definitions","JSON Schema validator library (typically built-in or lightweight dependency)"],"input_types":["MCP tool call (tool name + arguments)","tool schema definition (JSON Schema format)","validation strictness configuration (allow unknown fields, coerce types, etc.)"],"output_types":["validation result (pass/fail)","validation error details (field name, constraint violated, expected type)","sanitized/coerced arguments (if configured)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-promptspeak-mcp-server__cap_4","uri":"capability://safety.moderation.configurable.policy.engine.for.tool.access.control","name":"configurable policy engine for tool access control","description":"Provides a declarative policy language or configuration format for defining which tools can be called under which conditions, supporting role-based access control, resource-based policies, and context-dependent rules. Policies are evaluated against tool call context (caller identity, tool name, arguments, execution environment) to make allow/deny decisions.","intents":["I want to define fine-grained access control policies for tools without writing code","I need to implement role-based tool access where different users/agents can call different tools","I want to restrict certain tools to specific execution contexts (e.g., only in production, only for certain data types)"],"best_for":["multi-tenant AI systems where different users have different tool permissions","enterprises with complex compliance requirements for tool access control","teams wanting to manage tool permissions through configuration rather than code"],"limitations":["Policy language complexity vs. expressiveness tradeoff — simple languages cannot express complex rules, complex languages require expertise","No built-in support for dynamic policy updates without server restart (unless custom hot-reload is implemented)","Policy evaluation adds latency proportional to policy complexity — deeply nested conditions can impact performance","Debugging policy behavior can be difficult — policy interactions and edge cases may not be obvious"],"requires":["Node.js 16+","Policy configuration file (JSON, YAML, or custom DSL) or API","Understanding of policy language syntax and semantics"],"input_types":["policy definition (declarative format: JSON, YAML, or custom DSL)","execution context (caller identity, tool name, arguments, environment variables)","policy evaluation request"],"output_types":["policy decision (allow/deny/hold)","decision rationale (which policy rule matched)","applicable constraints or modifications to the call"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-promptspeak-mcp-server__cap_5","uri":"capability://automation.workflow.circuit.breaker.pattern.for.tool.call.rate.limiting.and.failure.handling","name":"circuit breaker pattern for tool call rate limiting and failure handling","description":"Implements circuit breaker logic to prevent cascading failures when tools become unavailable or start failing repeatedly. Tracks tool call success/failure rates and automatically opens the circuit (blocks calls) when failure rate exceeds threshold, with configurable recovery strategies (exponential backoff, manual reset, or gradual reopening).","intents":["I want to prevent agents from repeatedly calling a failing tool and wasting resources","I need to protect downstream systems from being overwhelmed by agent tool calls during outages","I want automatic recovery from transient tool failures without manual intervention"],"best_for":["agents calling external APIs or services that may become unavailable","systems where tool failures can cascade and cause broader system degradation","teams wanting resilience without implementing complex retry logic in agent code"],"limitations":["Circuit breaker state is local to the server instance — distributed systems need shared state (Redis, database) for consistent behavior","Threshold tuning is application-specific — no one-size-fits-all configuration for failure rate or recovery strategy","Blocks all calls when circuit is open, even if some calls might succeed — may be overly aggressive for some use cases"],"requires":["Node.js 16+","Configuration for circuit breaker thresholds (failure rate, sample window, open timeout)","Optional: shared state store (Redis, database) for distributed circuit breaker state"],"input_types":["tool call attempt","tool call result (success/failure)","circuit breaker configuration (thresholds, recovery strategy)"],"output_types":["circuit breaker state (closed/open/half-open)","decision (allow call / block with circuit-open error)","metrics (failure rate, call count, state transitions)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-promptspeak-mcp-server__cap_6","uri":"capability://safety.moderation.audit.logging.and.compliance.tracking.for.all.tool.calls","name":"audit logging and compliance tracking for all tool calls","description":"Records comprehensive audit logs of all tool calls, including caller identity, tool name, arguments, execution result, decision rationale (if blocked/held), and timestamps. Logs are structured for compliance reporting and forensic analysis, with support for exporting to external audit systems or compliance frameworks.","intents":["I need to maintain audit trails of all agent tool calls for compliance and regulatory requirements","I want to investigate what an agent did and why, including decisions to block or hold calls","I need to export audit logs to compliance systems or for forensic analysis of agent behavior"],"best_for":["regulated industries (finance, healthcare, government) with mandatory audit trail requirements","teams needing forensic analysis capabilities for security incidents or agent misbehavior","organizations building customer-facing AI systems that need transparency and accountability"],"limitations":["Audit logging adds latency and storage overhead — high-frequency tool calls can generate large log volumes","Log storage is not included — requires external logging system (file, database, cloud logging service)","Sensitive data in logs (API keys, user data) requires careful handling and redaction","Log retention and archival policies must be configured separately"],"requires":["Node.js 16+","External logging system or file storage for audit logs","Configuration for log format, retention, and export"],"input_types":["tool call event (caller, tool name, arguments, result)","governance decision (allow/block/hold with rationale)","execution context (timestamp, environment, request ID)"],"output_types":["structured audit log entry (JSON or custom format)","exported audit report (CSV, JSON, or compliance format)","audit summary (call counts, decision distribution, anomalies)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-promptspeak-mcp-server__cap_7","uri":"capability://tool.use.integration.mcp.server.integration.and.protocol.compatibility","name":"mcp server integration and protocol compatibility","description":"Implements the Model Context Protocol (MCP) server specification, exposing governance capabilities as MCP resources and tools that can be called by MCP-compatible clients. Handles MCP message parsing, routing, and response formatting, with support for both stdio and HTTP transport protocols.","intents":["I want to integrate governance capabilities into my MCP-compatible client without modifying client code","I need to use this governance server with Claude Desktop, Anthropic SDK, or custom MCP clients","I want to expose governance policies and decisions through standard MCP interfaces"],"best_for":["developers building MCP-compatible clients (Claude Desktop, custom agents, Anthropic SDK users)","teams wanting to add governance to existing MCP setups without rewriting infrastructure","organizations standardizing on MCP for AI agent tool access"],"limitations":["MCP protocol overhead — adds latency compared to direct function calls","Limited to MCP-compatible clients — cannot be used with non-MCP systems without adapters","Protocol version compatibility — requires clients to support the MCP version implemented by the server"],"requires":["Node.js 16+","MCP-compatible client (Claude Desktop, Anthropic SDK with MCP support, or custom MCP client)","Transport configuration (stdio for local, HTTP for remote)"],"input_types":["MCP protocol messages (JSON-RPC format)","tool call requests with MCP schema"],"output_types":["MCP protocol responses (JSON-RPC format)","tool execution results or governance decisions"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (MCP server runtime)","MCP-compatible client (Claude Desktop, custom MCP client, or Anthropic SDK with MCP support)","Policy configuration file or API for defining blocking rules","Node.js 16+","External approval backend or webhook endpoint to receive approval notifications","Mechanism to notify humans of pending approvals (email, Slack, custom dashboard)","Client capable of handling async approval responses (not all MCP clients support this pattern)","Time-series data storage for tool call history (in-memory, database, or external service)","Minimum baseline period (typically 100+ calls) before drift detection is reliable","Configuration for statistical thresholds (z-score, percentile, or custom anomaly detection algorithm)"],"failure_modes":["Blocking is deterministic only — cannot handle probabilistic or context-dependent policies without custom rule logic","No built-in support for dynamic policy updates without server restart unless custom persistence layer is added","Performance depends on policy rule complexity — deeply nested conditions can add latency to every tool call","Adds latency to agent execution — human approval time is unpredictable and can block agent progress indefinitely","Requires external approval infrastructure (queue, notification system, approval UI) — not included in the server itself","No built-in timeout mechanism — held calls can remain pending indefinitely if approval is never received","Stateful design requires persistence layer to survive server restarts without losing pending approvals","Requires historical baseline data — new agents have no baseline and cannot detect drift until sufficient history is collected","Statistical detection produces false positives and false negatives — tuning sensitivity requires domain knowledge","Cannot distinguish between legitimate behavior changes and actual drift without additional context","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.482Z","last_scraped_at":"2026-05-03T14:24:06.317Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-promptspeak-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=npm-promptspeak-mcp-server"}},"signature":"v69GuD/b6+q347Z2AV3PdGjS7s3lPty0xE1CLHoweBQeOvLaFPy96tHzIu0hZioyebuePO2cIA9W2DdE6/eTCg==","signedAt":"2026-06-20T09:38:53.435Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-promptspeak-mcp-server","artifact":"https://unfragile.ai/npm-promptspeak-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=npm-promptspeak-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}