{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_actiongate-actiongate","slug":"actiongate-actiongate","name":"ActionGate","type":"mcp","url":"https://smithery.ai/servers/actiongate/actiongate","page_url":"https://unfragile.ai/actiongate-actiongate","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:actiongate/actiongate"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_actiongate-actiongate__cap_0","uri":"capability://data.processing.analysis.risk.score.evaluation.and.quantification","name":"risk score evaluation and quantification","description":"Computes numerical risk scores for transactions, decisions, or business events by applying configurable scoring models through MCP tool endpoints. The system accepts transaction context (amount, user profile, historical patterns, geographic data) and returns normalized risk scores (typically 0-100 or 0-1 scale) that indicate likelihood of fraud, default, or policy violation. Scoring logic is abstracted behind MCP tool interfaces, allowing pluggable risk models (rule-based, ML-based, or hybrid) without client-side implementation.","intents":["I need to assign a risk score to a transaction before deciding whether to approve it","I want to evaluate multiple risk dimensions (fraud, compliance, operational) for a single decision","I need consistent risk quantification across different business processes and systems"],"best_for":["FinTech teams building fraud detection into payment flows","Risk and compliance officers automating decision gates","Platform teams integrating risk assessment into multi-step workflows"],"limitations":["Risk model accuracy depends entirely on input feature quality and training data; garbage-in-garbage-out applies","Real-time scoring latency depends on model complexity; complex ensemble models may exceed sub-100ms SLAs","No built-in model versioning or A/B testing framework — requires external orchestration for model rollouts"],"requires":["MCP client capable of calling tool endpoints","Transaction or decision context formatted according to ActionGate schema","Pre-configured risk model deployed on ActionGate server"],"input_types":["structured JSON transaction object","user/entity profile data","historical event sequences"],"output_types":["numeric risk score (float 0-100 or 0-1)","risk category (low/medium/high)","confidence/uncertainty metric"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_actiongate-actiongate__cap_1","uri":"capability://planning.reasoning.outcome.simulation.and.decision.impact.forecasting","name":"outcome simulation and decision impact forecasting","description":"Simulates the predicted consequences of approving, rejecting, or conditionally gating a transaction or decision by running forward-looking models that estimate downstream effects (revenue impact, fraud loss, customer churn, compliance risk). The simulation engine accepts a proposed action and current context, then returns projected outcomes across multiple dimensions (financial, operational, regulatory). This enables decision-makers to evaluate trade-offs before committing to a policy.","intents":["I want to see what happens if I approve this high-risk transaction — what's the expected loss vs revenue gain?","I need to forecast the impact of tightening my fraud policy on customer conversion rates","I want to compare outcomes across different gating strategies (approve, reject, challenge) before deciding"],"best_for":["Risk managers and compliance teams evaluating policy changes","Product teams optimizing conversion vs fraud trade-offs","Agents and automation systems that need to reason about decision consequences"],"limitations":["Simulation accuracy is bounded by historical data quality and model calibration; rare events (tail risks) are inherently harder to forecast","Simulations assume stationary environments; sudden market shifts or adversarial behavior changes can invalidate predictions","No causal inference framework — correlations in training data may not reflect true causal relationships"],"requires":["MCP client with tool-calling capability","Historical outcome data used to train simulation models","Transaction/decision context and proposed action specification"],"input_types":["current transaction/decision context (structured JSON)","proposed action (approve/reject/challenge/conditional)","optional: custom outcome dimensions to forecast"],"output_types":["projected outcome metrics (revenue, fraud loss, churn rate, etc.)","confidence intervals or uncertainty bounds","comparative outcome summary across action alternatives"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_actiongate-actiongate__cap_2","uri":"capability://automation.workflow.policy.driven.transaction.gating.with.conditional.enforcement","name":"policy-driven transaction gating with conditional enforcement","description":"Enforces business policies by evaluating transactions against configurable rule sets and decision trees, then returning a gate decision (approve, reject, challenge, escalate) with optional conditions (e.g., 'approve if amount < $5000 and risk_score < 40'). The gating engine applies policies in sequence, short-circuiting on hard blocks and accumulating soft constraints. Policies are defined declaratively (not in code) and can reference risk scores, user attributes, historical patterns, and external signals. Decisions include metadata (policy rule matched, confidence, remediation steps) for audit and debugging.","intents":["I want to automatically approve low-risk transactions and reject high-risk ones based on my fraud policy","I need to apply different policies to different user segments (new vs returning customers)","I want to challenge suspicious transactions with additional verification instead of outright rejection"],"best_for":["Compliance and risk teams automating policy enforcement","Payment processors and platforms managing transaction throughput at scale","Teams that need rapid policy iteration without code deployment"],"limitations":["Policy evaluation latency scales with rule complexity; deeply nested decision trees may add 50-200ms per transaction","No built-in conflict resolution for contradictory policies — requires explicit priority ordering","Policies are stateless per transaction; no memory of previous decisions for the same user within a single policy evaluation"],"requires":["MCP client capable of calling tool endpoints","Pre-defined policy rules in ActionGate's policy definition language","Transaction context with all attributes referenced in policies"],"input_types":["transaction object with amount, user ID, merchant, timestamp, etc.","risk score (from risk evaluation capability)","user profile and historical attributes"],"output_types":["gate decision (approve/reject/challenge/escalate)","decision metadata (policy rule matched, confidence, reason)","optional: remediation steps or additional verification requirements"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_actiongate-actiongate__cap_3","uri":"capability://tool.use.integration.real.time.decision.integration.into.automated.workflows","name":"real-time decision integration into automated workflows","description":"Embeds ActionGate decision endpoints directly into MCP-based automation workflows, allowing orchestration systems (agents, workflow engines, CI/CD pipelines) to call risk evaluation, simulation, and gating tools as native steps. Decisions are returned synchronously with full context, enabling downstream workflow steps to branch based on gate outcomes (e.g., approve → process payment; reject → send decline notice; challenge → trigger 2FA). The integration is protocol-native (MCP tools), eliminating the need for custom API wrappers or polling loops.","intents":["I want to insert a risk gate into my payment processing workflow without building custom integrations","I need my automation system to make intelligent decisions based on risk scores and policies","I want to log and audit all gating decisions as part of my workflow execution trace"],"best_for":["Platform teams building MCP-native automation systems","Agents and LLM-based orchestration systems that need to make secure decisions","Teams migrating from REST-based decision APIs to MCP"],"limitations":["Workflow latency is additive — each ActionGate tool call adds network round-trip time (typically 50-200ms per call)","No built-in workflow state persistence — requires external state store if decisions must be replayed or audited","MCP protocol overhead may be significant for high-frequency, low-latency use cases (e.g., microsecond-scale trading)"],"requires":["MCP client or workflow engine with tool-calling support","ActionGate MCP server deployed and accessible","Workflow definition that includes ActionGate tool calls as steps"],"input_types":["workflow context (transaction, user, decision parameters)","tool invocation parameters (risk evaluation, simulation, gating)"],"output_types":["gate decision with metadata","workflow branching signal (approve/reject/challenge)","optional: simulation results for downstream decision logic"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_actiongate-actiongate__cap_4","uri":"capability://data.processing.analysis.multi.dimensional.risk.assessment.with.configurable.scoring.models","name":"multi-dimensional risk assessment with configurable scoring models","description":"Evaluates risk across multiple independent dimensions (fraud risk, compliance risk, operational risk, customer lifetime value risk) by running parallel or sequential scoring models and aggregating results into a composite risk profile. Each dimension uses a specialized model (e.g., fraud detection uses gradient boosting; compliance uses rule-based scoring) and returns both a score and contributing factors. The system supports weighted aggregation, allowing different dimensions to contribute differently to the final decision. Scoring models are pluggable and can be swapped without changing client code.","intents":["I need to evaluate fraud risk AND compliance risk for the same transaction","I want to weight fraud risk more heavily than operational risk in my final decision","I need to understand which risk dimensions are driving a particular decision"],"best_for":["Risk teams managing multiple risk types (fraud, compliance, operational)","Organizations with complex regulatory requirements across jurisdictions","Teams that need explainability in risk decisions"],"limitations":["Aggregating multiple risk dimensions assumes independence; correlated risks may be double-counted","Weighting schemes are static per policy; dynamic weighting based on context requires custom logic","No built-in sensitivity analysis — teams must externally test how weight changes affect decisions"],"requires":["MCP client","Pre-trained scoring models for each risk dimension","Aggregation weights and combination logic defined in policy"],"input_types":["transaction/decision context","optional: custom weights for each risk dimension"],"output_types":["per-dimension risk scores","contributing factors per dimension","composite risk score","optional: sensitivity analysis (how score changes with input variations)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_actiongate-actiongate__cap_5","uri":"capability://automation.workflow.decision.audit.logging.and.compliance.reporting","name":"decision audit logging and compliance reporting","description":"Automatically logs all gating decisions (approve/reject/challenge) with full context (transaction details, risk scores, policy rules matched, timestamp, user ID) to an audit trail. Logs include decision metadata (confidence, contributing factors, alternative outcomes) enabling post-hoc analysis and compliance reporting. The audit trail is queryable and exportable, supporting regulatory requirements (PCI-DSS, GDPR, SOX) that mandate decision documentation. Logs are immutable (append-only) and include cryptographic signatures for tamper-evidence.","intents":["I need to prove to auditors that my transaction decisions followed my stated policies","I want to analyze why a particular transaction was rejected to improve my policy","I need to generate compliance reports showing decision distribution and policy adherence"],"best_for":["Regulated financial institutions (banks, payment processors, lending platforms)","Compliance and audit teams","Teams subject to SOX, PCI-DSS, GDPR, or similar regulatory frameworks"],"limitations":["Audit log storage scales linearly with transaction volume; high-frequency systems may require external log aggregation (e.g., ELK, Splunk)","Immutable logs prevent correction of erroneous entries; requires separate amendment records for corrections","Querying large audit logs for compliance reports may require external analytics tools"],"requires":["MCP client","Audit log storage backend (local or remote)","Optional: log aggregation and analytics system"],"input_types":["decision context (transaction, risk scores, policy rules)","decision outcome (approve/reject/challenge)"],"output_types":["audit log entry (structured JSON or CSV)","compliance report (decision distribution, policy adherence metrics)","optional: tamper-evidence certificate"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_actiongate-actiongate__cap_6","uri":"capability://automation.workflow.user.segmentation.and.policy.differentiation","name":"user segmentation and policy differentiation","description":"Applies different policies and risk thresholds to different user segments (new vs returning customers, high-value vs low-value, geographic regions, risk tiers) by evaluating user attributes and historical behavior to determine segment membership, then routing to segment-specific policies. Segmentation logic is declarative and can reference user profile, transaction history, and external signals. Each segment has independent risk thresholds, approval rates, and challenge strategies, enabling tailored decision-making without duplicating core logic.","intents":["I want to be more lenient with my high-value customers and stricter with new users","I need to apply different policies to customers in different countries due to regulatory requirements","I want to gradually relax policies for users as they build transaction history"],"best_for":["Platforms with diverse user bases (new vs established, high vs low value)","Multi-jurisdictional businesses with region-specific regulatory requirements","Teams optimizing conversion by reducing friction for trusted users"],"limitations":["Segment membership evaluation adds latency; complex segmentation logic may add 20-50ms per decision","Segment definitions are static per policy evaluation; dynamic segmentation based on real-time signals requires external computation","No built-in segment overlap handling — overlapping segment definitions require explicit priority ordering"],"requires":["MCP client","User profile data with attributes used for segmentation","Segment definitions and policy mappings"],"input_types":["user profile (ID, account age, lifetime value, geographic location, etc.)","transaction context","optional: custom segment attributes"],"output_types":["determined user segment","segment-specific policy applied","segment-specific decision (approve/reject/challenge)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_actiongate-actiongate__cap_7","uri":"capability://automation.workflow.conditional.challenge.workflows.with.adaptive.verification","name":"conditional challenge workflows with adaptive verification","description":"Routes transactions flagged as medium-risk to challenge workflows (additional verification steps like 2FA, identity verification, or manual review) instead of outright rejection. Challenge strategies are configurable per policy and can adapt based on risk score, user segment, and transaction context (e.g., high-value transactions require manual review; low-value require 2FA). The system tracks challenge outcomes (user completed verification, failed, abandoned) and feeds results back into risk models to improve future scoring. Challenge workflows are defined declaratively and can integrate with external verification providers (SMS, email, biometric).","intents":["I want to challenge suspicious transactions instead of rejecting them outright to reduce false positives","I need different verification methods for different risk levels (2FA for medium risk, manual review for high risk)","I want to track whether users complete challenges and use that signal to improve my risk models"],"best_for":["Platforms balancing fraud prevention with user experience (e.g., payment processors, lending platforms)","Teams with manual review capacity for high-risk transactions","Organizations that can integrate with external verification providers"],"limitations":["Challenge workflows introduce user friction and abandonment; conversion impact must be measured and optimized","Challenge outcomes (completion, failure) are asynchronous; requires callback mechanism to update decision records","No built-in challenge outcome prediction — teams must externally analyze which challenge types are most effective"],"requires":["MCP client","External verification provider integrations (SMS, email, biometric, etc.)","Challenge workflow definitions","Callback mechanism to handle challenge outcomes"],"input_types":["transaction context","risk score and risk factors","user segment and preferences"],"output_types":["challenge decision (which verification method to use)","challenge workflow specification (steps, timeout, retry logic)","optional: challenge outcome callback"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_actiongate-actiongate__cap_8","uri":"capability://automation.workflow.real.time.policy.updates.without.service.restart","name":"real-time policy updates without service restart","description":"Allows policy rules and risk thresholds to be updated in real-time through MCP tool endpoints without restarting the ActionGate server or redeploying client applications. Policy changes are versioned and timestamped, enabling rollback if needed. The system supports gradual rollout (canary deployment) of new policies by applying them to a percentage of transactions and comparing outcomes. Policy changes are immediately effective for new transactions while maintaining consistency for in-flight decisions.","intents":["I want to tighten my fraud policy in response to a new fraud pattern without restarting my system","I need to test a new policy on 10% of transactions before rolling it out to everyone","I want to quickly revert a policy change if it causes unexpected side effects"],"best_for":["Teams that need rapid policy iteration in response to fraud trends","Organizations with high transaction volume that can't afford service restarts","Teams using canary deployments and A/B testing for policy optimization"],"limitations":["In-flight transactions may see inconsistent policies if updates occur during processing; requires idempotency guarantees","Policy version management adds complexity; teams must track which version was applied to each transaction","Rollback is not instantaneous — transactions in progress may still use the old policy"],"requires":["MCP client with tool-calling capability","Policy versioning and storage backend","Optional: canary deployment infrastructure"],"input_types":["new policy rules (declarative format)","optional: rollout percentage (for canary deployment)"],"output_types":["policy update confirmation (version, timestamp, affected rules)","optional: canary deployment metrics (transactions affected, outcome distribution)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["MCP client capable of calling tool endpoints","Transaction or decision context formatted according to ActionGate schema","Pre-configured risk model deployed on ActionGate server","MCP client with tool-calling capability","Historical outcome data used to train simulation models","Transaction/decision context and proposed action specification","Pre-defined policy rules in ActionGate's policy definition language","Transaction context with all attributes referenced in policies","MCP client or workflow engine with tool-calling support","ActionGate MCP server deployed and accessible"],"failure_modes":["Risk model accuracy depends entirely on input feature quality and training data; garbage-in-garbage-out applies","Real-time scoring latency depends on model complexity; complex ensemble models may exceed sub-100ms SLAs","No built-in model versioning or A/B testing framework — requires external orchestration for model rollouts","Simulation accuracy is bounded by historical data quality and model calibration; rare events (tail risks) are inherently harder to forecast","Simulations assume stationary environments; sudden market shifts or adversarial behavior changes can invalidate predictions","No causal inference framework — correlations in training data may not reflect true causal relationships","Policy evaluation latency scales with rule complexity; deeply nested decision trees may add 50-200ms per transaction","No built-in conflict resolution for contradictory policies — requires explicit priority ordering","Policies are stateless per transaction; no memory of previous decisions for the same user within a single policy evaluation","Workflow latency is additive — each ActionGate tool call adds network round-trip time (typically 50-200ms per call)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6281710990542908,"quality":0.43,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:18:27.094Z","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=actiongate-actiongate","compare_url":"https://unfragile.ai/compare?artifact=actiongate-actiongate"}},"signature":"/BLZBh8i30VXso2ePu5D+cj9srSRUpQhsKsyifD28icCTNBuNqNiVxb6Ph4MhpFnQv21P9zf7d3jT5RBRTwVBw==","signedAt":"2026-06-21T17:56:07.851Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/actiongate-actiongate","artifact":"https://unfragile.ai/actiongate-actiongate","verify":"https://unfragile.ai/api/v1/verify?slug=actiongate-actiongate","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"}}