{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-footprintjs","slug":"npm-footprintjs","name":"footprintjs","type":"mcp","url":"https://www.npmjs.com/package/footprintjs","page_url":"https://unfragile.ai/npm-footprintjs","categories":["mcp-servers"],"tags":["explainability","xai","ai-agent","mcp","decision-engine","rule-engine","audit-trail","causal-trace","flowchart","pipeline","workflow","llm","openapi","narrative","observability","tracing","state-management","time-travel-debugging","typescript","footprint"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-footprintjs__cap_0","uri":"capability://automation.workflow.automatic.causal.trace.generation.for.backend.flows","name":"automatic causal trace generation for backend flows","description":"Automatically instruments backend execution paths to generate causal traces showing how data flows through functions, API calls, and decision points. Uses AST analysis and runtime instrumentation to capture the dependency graph between inputs, intermediate states, and outputs without requiring manual annotation. Traces are structured as directed acyclic graphs (DAGs) that can be serialized and replayed for debugging or audit purposes.","intents":["I need to understand why my backend made a specific decision without manually tracing through logs","I want to generate audit trails that show exactly which data influenced a particular output","I need to replay a production bug by reconstructing the exact execution path and state transitions","I want to visualize how data dependencies flow through my microservices for compliance documentation"],"best_for":["backend engineers building complex decision systems or rule engines","teams operating regulated systems requiring detailed audit trails","developers integrating AI agents with deterministic backend logic","platform teams building observability infrastructure"],"limitations":["Instrumentation overhead scales with function call depth — deep call stacks may add 50-200ms per trace","Requires source code access for AST analysis — cannot instrument compiled binaries or third-party closed-source libraries","Trace storage grows linearly with execution complexity — high-volume systems need external persistence layer","Limited to synchronous execution paths — async/await and Promise chains require additional instrumentation"],"requires":["Node.js 14+ or TypeScript 4.5+","Access to source code or transpiled JavaScript with source maps","Optional: external storage system (PostgreSQL, MongoDB) for trace persistence"],"input_types":["TypeScript/JavaScript function definitions","Backend API handlers (Express, Fastify, etc.)","Rule engine configurations","Decision tree logic"],"output_types":["JSON-serialized DAG structure","Trace visualization data","Audit log entries","State snapshot sequences"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-footprintjs__cap_1","uri":"capability://planning.reasoning.decision.evidence.extraction.and.narrative.generation","name":"decision evidence extraction and narrative generation","description":"Extracts the evidence, conditions, and decision rules that led to a specific backend outcome, then generates human-readable narratives explaining the decision chain. Analyzes the causal trace to identify which inputs were actually used in the decision (vs. which were available but ignored), reconstructs the logical conditions that were evaluated, and produces structured evidence objects that can be presented to users or AI agents. Supports template-based narrative generation for different audiences (technical, business, regulatory).","intents":["I need to explain to a user why their loan application was denied with specific evidence","I want to generate compliance reports showing which rules and data points drove a decision","I need to provide an AI agent with structured evidence about why a backend system rejected a request","I want to create audit documentation that shows decision rationale for regulatory review"],"best_for":["fintech and lending platforms requiring explainable decisions","compliance and risk teams documenting decision rationale","AI agent builders integrating with rule-based backend systems","customer support teams explaining system decisions to end users"],"limitations":["Evidence extraction accuracy depends on code clarity — obfuscated or dynamically-generated logic may produce incomplete evidence","Narrative generation templates are domain-specific — generic templates may not capture business context","Cannot extract evidence from external service calls where logic is opaque (third-party APIs, ML models)","Requires explicit mapping between code conditions and business rule names for readable narratives"],"requires":["Causal trace data from automatic trace generation capability","TypeScript/JavaScript source code with readable variable and function names","Optional: custom narrative templates or domain-specific rule mappings"],"input_types":["Causal trace DAG","Source code context","Rule definitions or decision tree specifications","Template configuration (Handlebars, custom format)"],"output_types":["Structured evidence objects (JSON)","Human-readable narratives (Markdown, plain text)","Compliance reports","Decision explanation objects for API responses"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-footprintjs__cap_2","uri":"capability://tool.use.integration.mcp.tool.schema.generation.from.backend.flows","name":"mcp tool schema generation from backend flows","description":"Automatically generates Model Context Protocol (MCP) tool definitions from instrumented backend functions and API endpoints, creating structured schemas that describe inputs, outputs, side effects, and decision logic. Analyzes the causal traces and evidence extraction to infer tool semantics (e.g., 'this function filters users by criteria and returns a ranked list'), generates OpenAPI-compatible schemas with proper type definitions, and produces MCP tool manifests that AI agents can consume. Includes automatic documentation generation from code comments and inferred behavior.","intents":["I want my AI agent to call backend functions with proper type safety and schema validation","I need to expose my backend decision logic as tools that Claude or other LLMs can use","I want to automatically generate OpenAPI specs from my instrumented backend code","I need to ensure AI agents understand the side effects and constraints of backend operations"],"best_for":["AI agent builders integrating with existing backend systems","teams building LLM-powered applications that need deterministic backend operations","API teams automating OpenAPI documentation generation","platform teams exposing internal tools to AI agents with proper governance"],"limitations":["Schema inference works best with explicit type annotations — untyped JavaScript may produce incomplete schemas","Side effect detection relies on code analysis — external state mutations (database writes, API calls) may be missed if not explicitly tracked","Generated schemas may require manual refinement for complex domain-specific types or polymorphic operations","Does not automatically generate error handling schemas — error cases must be explicitly documented"],"requires":["TypeScript with type annotations (or JSDoc type comments for JavaScript)","Instrumented backend functions with causal traces","Node.js 14+ and MCP SDK or compatible framework","Optional: OpenAPI 3.0+ compatible API documentation"],"input_types":["TypeScript function signatures","Backend API endpoint definitions","Causal trace metadata","Code comments and documentation"],"output_types":["MCP tool manifests (JSON)","OpenAPI schema definitions (YAML/JSON)","TypeScript type definitions","Tool documentation (Markdown)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-footprintjs__cap_3","uri":"capability://automation.workflow.time.travel.debugging.with.state.snapshots","name":"time-travel debugging with state snapshots","description":"Captures immutable state snapshots at each step of a causal trace, enabling developers to inspect the exact state of variables, function arguments, and return values at any point in the execution. Provides a queryable interface to jump to specific trace steps, inspect state diffs between consecutive steps, and replay execution from any checkpoint. Uses structural sharing and delta compression to minimize memory overhead while maintaining full state history.","intents":["I want to step through a production bug without re-running it, inspecting state at each decision point","I need to compare state differences between two execution paths to understand why they diverged","I want to replay execution from a specific point to test a hypothetical code change","I need to debug race conditions by examining the exact sequence of state mutations"],"best_for":["backend engineers debugging complex stateful systems","teams investigating production incidents with reproducible traces","developers building deterministic systems where replay is critical","QA teams creating regression test cases from captured traces"],"limitations":["State snapshot storage grows with execution length — traces with 1000+ steps may require 10-50MB per execution","Cannot capture state of external resources (database connections, file handles) — only in-process state","Snapshots are taken at function boundaries — intermediate state within a function is not captured","Large object graphs (circular references, deep nesting) may cause snapshot serialization overhead"],"requires":["Causal trace generation enabled","Node.js 14+ with sufficient memory for state snapshots","Optional: external storage for long-term trace retention"],"input_types":["Causal trace with instrumented execution","Query parameters (step number, variable name, state path)"],"output_types":["State snapshot objects (JSON)","State diff objects (JSON Patch format)","Replay execution results","Visualization data for debugger UI"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-footprintjs__cap_4","uri":"capability://planning.reasoning.rule.engine.integration.and.decision.tree.visualization","name":"rule engine integration and decision tree visualization","description":"Integrates with rule engines and decision tree systems to automatically instrument rule evaluation, capture which rules matched/failed, and visualize the decision tree structure with execution paths highlighted. Supports multiple rule engine formats (JSON-based rules, Drools-style syntax, custom DSLs) and generates interactive flowchart visualizations showing the decision path taken during execution. Includes rule conflict detection and coverage analysis to identify unreachable rules or conflicting conditions.","intents":["I want to visualize which rules fired in my decision engine and why others didn't match","I need to detect conflicting rules that could cause non-deterministic behavior","I want to analyze rule coverage to find untested decision paths","I need to explain a rule-based decision to a business stakeholder with a flowchart"],"best_for":["teams operating rule-based decision systems (lending, insurance, fraud detection)","business analysts validating rule logic without code review","compliance teams auditing rule-based decisions","developers debugging complex rule interactions"],"limitations":["Rule format support is limited — custom DSLs require custom instrumentation adapters","Visualization complexity grows exponentially with rule count — 100+ rules may produce unreadable flowcharts","Conflict detection is static analysis only — cannot detect conflicts that only manifest at runtime with specific data","Rule coverage analysis requires test case generation — not automatically derived from production traces"],"requires":["Rule engine or decision tree system integrated with footprintjs","Rule definitions in supported format (JSON, YAML, or custom DSL with adapter)","Node.js 14+ and visualization library (D3.js, Mermaid, or custom)"],"input_types":["Rule definitions (JSON, YAML, DSL)","Causal trace with rule evaluation metadata","Input data that triggered rule evaluation"],"output_types":["Flowchart visualization (SVG, Mermaid, interactive HTML)","Rule conflict report (JSON)","Coverage analysis report (JSON, HTML)","Decision path explanation (text, JSON)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-footprintjs__cap_5","uri":"capability://automation.workflow.pipeline.state.management.and.workflow.orchestration","name":"pipeline state management and workflow orchestration","description":"Provides state management for multi-step backend workflows and pipelines, automatically tracking state transitions, validating state changes against defined schemas, and enabling rollback to previous states. Integrates with causal tracing to record why state changed (which function triggered it, what conditions were met), and supports compensation logic for undoing operations in reverse order. Includes built-in support for saga patterns and distributed transaction coordination across service boundaries.","intents":["I need to track state changes through a multi-step workflow and be able to rollback if something fails","I want to implement saga patterns for distributed transactions without manual compensation logic","I need to validate that state transitions follow defined rules (e.g., order can only move from 'pending' to 'confirmed')","I want to understand the complete state history of a workflow for audit and debugging purposes"],"best_for":["backend teams building complex workflows (order processing, loan origination, onboarding)","microservices teams implementing distributed transactions","teams requiring strong audit trails for state changes","developers building event-sourced systems"],"limitations":["Rollback is logical only — does not automatically undo external side effects (database writes, API calls) without explicit compensation handlers","State schema validation adds latency — complex schemas with deep nesting may add 10-50ms per transition","Distributed transaction coordination requires network calls — high-latency services may cause timeout issues","Compensation logic must be explicitly defined — no automatic reversal of arbitrary operations"],"requires":["TypeScript with type annotations for state schema","Node.js 14+","Optional: message queue (RabbitMQ, Kafka) for distributed coordination","Optional: external state store (PostgreSQL, Redis) for persistence"],"input_types":["State schema definitions (TypeScript interfaces or JSON Schema)","Workflow step definitions","Compensation handlers (functions)","State transition rules"],"output_types":["State objects with metadata","State transition logs","Audit trail entries","Rollback confirmation"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-footprintjs__cap_6","uri":"capability://automation.workflow.observability.and.structured.logging.integration","name":"observability and structured logging integration","description":"Automatically generates structured logs from causal traces, integrating with standard observability platforms (Datadog, New Relic, CloudWatch, ELK). Converts trace data into structured log entries with proper correlation IDs, trace IDs, and span hierarchies compatible with OpenTelemetry standards. Enables querying and filtering logs by decision evidence, rule matches, and state changes rather than just text search. Includes automatic sampling and aggregation for high-volume systems to reduce storage costs.","intents":["I want to query my logs by decision evidence (e.g., 'show me all requests where rule X fired')","I need to correlate traces across services using standard trace IDs and span hierarchies","I want to sample high-volume logs intelligently based on decision outcomes or error rates","I need to export trace data to my existing observability platform without custom integration code"],"best_for":["platform teams operating high-volume systems requiring efficient observability","teams using observability platforms (Datadog, New Relic) who want richer trace data","developers building microservices with distributed tracing requirements","compliance teams needing queryable audit logs"],"limitations":["Structured log generation adds overhead — high-frequency operations may see 5-20% latency increase","Sampling strategies are heuristic-based — may miss rare edge cases if sampling is too aggressive","Integration with observability platforms requires API keys and network calls — adds external dependencies","Log volume can still be high for complex workflows — requires careful sampling configuration"],"requires":["Causal trace generation enabled","Node.js 14+","Optional: API keys for observability platforms (Datadog, New Relic, etc.)","Optional: OpenTelemetry SDK for distributed tracing"],"input_types":["Causal trace data","Configuration for observability platform","Sampling rules and filters"],"output_types":["Structured log entries (JSON)","OpenTelemetry spans","Observability platform events","Aggregated metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-footprintjs__cap_7","uri":"capability://automation.workflow.compliance.and.audit.report.generation","name":"compliance and audit report generation","description":"Automatically generates compliance and audit reports from causal traces, decision evidence, and state histories. Supports multiple report formats (PDF, HTML, JSON) and compliance frameworks (GDPR, HIPAA, SOX, Fair Lending). Includes data lineage tracking to show which personal data was used in decisions, automatic redaction of sensitive information, and proof of decision rationale for regulatory review. Generates attestation documents showing that decisions were made according to defined rules and policies.","intents":["I need to generate GDPR compliance reports showing what data was used in a decision","I want to create audit documentation proving that lending decisions followed fair lending rules","I need to redact sensitive information from traces before sharing with external auditors","I want to generate attestation documents showing that a decision was made according to policy"],"best_for":["fintech and lending teams subject to fair lending regulations","healthcare teams operating under HIPAA","any team handling personal data subject to GDPR","compliance and audit teams documenting decision rationale"],"limitations":["Report generation is template-based — custom compliance frameworks require custom templates","Data lineage tracking requires explicit annotation of sensitive fields — automatic detection is limited","Redaction is rule-based — complex PII patterns may require custom redaction logic","Attestation documents are generated from code — cannot verify that code actually enforces stated policies"],"requires":["Causal trace generation with decision evidence","Data schema with sensitivity annotations (PII, protected health info, etc.)","Compliance framework configuration (GDPR, HIPAA, etc.)","Optional: PDF generation library (pdfkit, puppeteer) for report output"],"input_types":["Causal trace with decision evidence","Data lineage metadata","Compliance framework rules","Redaction policies"],"output_types":["Compliance reports (PDF, HTML, JSON)","Audit trail documents","Attestation certificates","Data lineage diagrams","Redacted trace exports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or TypeScript 4.5+","Access to source code or transpiled JavaScript with source maps","Optional: external storage system (PostgreSQL, MongoDB) for trace persistence","Causal trace data from automatic trace generation capability","TypeScript/JavaScript source code with readable variable and function names","Optional: custom narrative templates or domain-specific rule mappings","TypeScript with type annotations (or JSDoc type comments for JavaScript)","Instrumented backend functions with causal traces","Node.js 14+ and MCP SDK or compatible framework","Optional: OpenAPI 3.0+ compatible API documentation"],"failure_modes":["Instrumentation overhead scales with function call depth — deep call stacks may add 50-200ms per trace","Requires source code access for AST analysis — cannot instrument compiled binaries or third-party closed-source libraries","Trace storage grows linearly with execution complexity — high-volume systems need external persistence layer","Limited to synchronous execution paths — async/await and Promise chains require additional instrumentation","Evidence extraction accuracy depends on code clarity — obfuscated or dynamically-generated logic may produce incomplete evidence","Narrative generation templates are domain-specific — generic templates may not capture business context","Cannot extract evidence from external service calls where logic is opaque (third-party APIs, ML models)","Requires explicit mapping between code conditions and business rule names for readable narratives","Schema inference works best with explicit type annotations — untyped JavaScript may produce incomplete schemas","Side effect detection relies on code analysis — external state mutations (database writes, API calls) may be missed if not explicitly tracked","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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-05-03T14:23:58.038Z","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-footprintjs","compare_url":"https://unfragile.ai/compare?artifact=npm-footprintjs"}},"signature":"V7F881bBcTurqMx8G+ZaJqu9JBjssRBDQBqcz5BUH1M+TejIDOstO/iPBNA1kCrAbKMQc0u6yqKOiBkG/+3UAA==","signedAt":"2026-06-21T00:07:22.240Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-footprintjs","artifact":"https://unfragile.ai/npm-footprintjs","verify":"https://unfragile.ai/api/v1/verify?slug=npm-footprintjs","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"}}