{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-pimzino-spec-workflow-mcp","slug":"mcp-pimzino-spec-workflow-mcp","name":"spec-workflow-mcp","type":"mcp","url":"https://github.com/Pimzino/spec-workflow-mcp","page_url":"https://unfragile.ai/mcp-pimzino-spec-workflow-mcp","categories":["mcp-servers","automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_0","uri":"capability://tool.use.integration.mcp.based.tool.registration.and.json.rpc.dispatch.for.ai.agents","name":"mcp-based tool registration and json-rpc dispatch for ai agents","description":"Implements a Model Context Protocol (MCP) server using StdioServerTransport that registers 13+ tools as JSON-RPC methods, enabling AI agents (Claude, Cursor, Codex) to invoke workflow operations through a standardized protocol. Tools return TOON-formatted responses with structured data and markdown content, abstracting the underlying file system and state management from the AI client.","intents":["I want Claude or Cursor to have access to structured workflow tools without custom integrations","I need to expose my spec workflow operations as callable functions to any MCP-compatible AI client","I want standardized JSON-RPC communication between my AI agent and the workflow system"],"best_for":["AI agent developers integrating with Claude Desktop or Cursor","Teams building spec-driven workflows with AI assistance","Developers wanting MCP-standard tool exposure without custom protocol work"],"limitations":["Stdio-based transport means single-threaded sequential request handling — concurrent tool calls from multiple agents require separate server instances","TOON format response wrapping adds serialization overhead for large artifact responses","No built-in request queuing or backpressure handling if AI client sends rapid-fire tool calls"],"requires":["Node.js 18+","MCP-compatible client (Claude Desktop, Cursor, or custom MCP host)","Project directory with .spec-workflow/ structure initialized"],"input_types":["JSON-RPC method parameters (strings, objects, arrays)","File paths and project identifiers"],"output_types":["TOON-formatted responses (text + markdown + structured data)","JSON objects with artifact metadata"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_1","uri":"capability://planning.reasoning.specification.driven.workflow.orchestration.with.sequential.phase.enforcement","name":"specification-driven workflow orchestration with sequential phase enforcement","description":"Enforces a strict sequential workflow (Requirements → Design → Tasks → Implementation → Approval) by tracking phase state in the .spec-workflow/ directory structure and preventing out-of-order transitions. Each phase has dedicated tools and storage locations (specs/, approvals/, steering/, archive/), with the system validating phase prerequisites before allowing progression and maintaining an immutable audit trail of all transitions.","intents":["I want to ensure my AI-assisted development follows a structured, repeatable workflow without skipping critical phases","I need to prevent developers from jumping to implementation before design is approved","I want an audit trail showing when each phase was completed and by whom"],"best_for":["Teams enforcing disciplined software development processes","Organizations requiring compliance-auditable development workflows","Projects where specification quality directly impacts implementation success"],"limitations":["Strict sequential enforcement can feel rigid for iterative or exploratory development — no built-in support for phase rollback or parallel work streams","Phase validation is file-system based, so concurrent modifications to phase state files can cause race conditions without external locking","No built-in support for conditional phase skipping (e.g., skipping design for trivial tasks)"],"requires":["Project initialized with .spec-workflow/ directory structure","Write access to project root for phase state files","Understanding of the 5-phase workflow model"],"input_types":["Phase identifiers (requirements, design, tasks, implementation, approval)","Specification documents (markdown, JSON)","Task definitions and implementation logs"],"output_types":["Phase state metadata (JSON)","Validation results (pass/fail with reasons)","Transition audit logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_10","uri":"capability://data.processing.analysis.file.system.based.state.persistence.with.environment.aware.storage.paths","name":"file system-based state persistence with environment-aware storage paths","description":"Stores all workflow state (.spec-workflow/ directory per project and ~/.spec-workflow-mcp/ global state) as files and directories, making state human-readable and version-controllable. The system supports environment variable overrides (SPEC_WORKFLOW_HOME) for sandboxed or containerized environments where $HOME is read-only, enabling deployment flexibility. State is organized hierarchically (specs/, tasks/, approvals/, archive/, implementation/) with each artifact as a separate file for granular version control.","intents":["I want my workflow state to be version-controllable and readable in git","I need to deploy the system in containerized environments with custom storage paths","I want to inspect and manually edit workflow state if needed"],"best_for":["Teams using git for all project artifacts","Containerized and cloud deployments with custom storage requirements","Projects where workflow transparency and auditability are critical"],"limitations":["File system-based state has no built-in concurrency control — multiple processes writing to the same files can cause corruption without external locking (e.g., flock, Redis)","Querying state requires scanning and parsing files — no indexing or fast lookups like a database would provide","File system performance degrades with large numbers of files (100k+ artifacts) — no built-in sharding or partitioning","Cross-platform file system differences (case sensitivity, path separators) can cause issues when moving projects between Windows and Unix systems"],"requires":["Write access to project root and home directory (or SPEC_WORKFLOW_HOME)","File system that supports directory creation and file I/O","Understanding of the .spec-workflow/ directory structure"],"input_types":["File paths","Directory structures","JSON and markdown file content"],"output_types":["File system artifacts","Directory hierarchies","Serialized state (JSON, markdown)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_11","uri":"capability://text.generation.language.internationalization.system.with.multi.language.ui.support","name":"internationalization system with multi-language ui support","description":"Provides an i18n system that enables the web dashboard and VSCode extension to render in multiple languages. Language files are stored as JSON objects mapping keys to translated strings, and the system detects the user's locale from browser/VSCode settings and loads the appropriate language file. This allows teams in different regions to use the system in their native language without requiring separate deployments.","intents":["I want the dashboard and VSCode extension to display in my native language","I need to support teams across multiple countries and languages","I want to add translations for new languages without modifying code"],"best_for":["Global teams with non-English speakers","Organizations expanding to international markets","Projects wanting to maximize accessibility"],"limitations":["i18n system is UI-only — workflow artifacts (specifications, tasks) are not automatically translated","Language detection is based on browser/VSCode locale settings — users can't manually override language without changing system settings","Translation maintenance requires manual updates for each language when UI text changes","No built-in translation management tool — adding new languages requires manually creating JSON files"],"requires":["Browser or VSCode with locale settings","Language JSON files in the i18n directory"],"input_types":["Language identifiers (en, es, fr, etc.)","Translation key-value pairs (JSON)"],"output_types":["Localized UI strings","Language-specific dashboard and extension UI"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_12","uri":"capability://automation.workflow.docker.containerization.with.multi.stage.builds.and.security.hardening","name":"docker containerization with multi-stage builds and security hardening","description":"Provides Dockerfile configurations for containerized deployment with multi-stage builds that separate build and runtime stages, reducing image size. The system includes security hardening (non-root user, minimal base image, read-only file system where possible) and supports both standard and prebuilt image variants. Docker Compose configuration enables easy local development with both MCP server and dashboard running in containers with proper networking and volume mounts.","intents":["I want to deploy spec-workflow-mcp in a containerized environment without manual setup","I need security hardening for production deployments","I want to run both MCP server and dashboard in Docker with proper networking"],"best_for":["Teams deploying to Kubernetes or Docker-based infrastructure","Organizations with container security requirements","Developers wanting reproducible, isolated development environments"],"limitations":["Docker deployment requires Docker and Docker Compose to be installed and running","Multi-stage builds add complexity to the Dockerfile — debugging build issues requires understanding Docker layer caching","Volume mounts for .spec-workflow/ directories must be configured correctly — incorrect mounts can cause permission issues or data loss","Prebuilt images are larger than building from source — pulling prebuilt images may be slower on slow networks"],"requires":["Docker 20.10+ and Docker Compose 2.0+","Sufficient disk space for image layers and container volumes","Understanding of Docker networking and volume mounts"],"input_types":["Dockerfile configuration","Docker Compose YAML","Project directories for volume mounts"],"output_types":["Docker images","Running containers","Container logs and metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_13","uri":"capability://safety.moderation.audit.logging.and.security.event.tracking.with.compliance.support","name":"audit logging and security event tracking with compliance support","description":"Records all significant events (tool invocations, approval decisions, phase transitions, file modifications) in audit logs stored in the .spec-workflow/ directory. Logs include timestamps, user identity, action type, and affected artifacts, enabling compliance audits and security investigations. The system supports structured logging formats (JSON) that can be ingested by SIEM systems or compliance tools for centralized monitoring.","intents":["I need to audit who did what and when for compliance requirements","I want to investigate security incidents by reviewing detailed event logs","I need to export audit logs to external compliance systems"],"best_for":["Organizations with compliance requirements (SOC 2, ISO 27001, HIPAA, etc.)","Security teams investigating incidents or anomalies","Projects requiring detailed change tracking for regulatory audits"],"limitations":["Audit logs are file-based — no built-in log rotation or archival, requiring manual cleanup to prevent disk space issues","User identity is captured from environment variables or git config — no built-in authentication or identity verification","Logs are stored locally — no built-in replication or backup, requiring external systems for log retention","No built-in alerting on suspicious events — requires external log analysis tools to detect anomalies"],"requires":["Write access to .spec-workflow/ directory for log files","User identity configuration (git config or environment variables)","External log analysis tools for compliance reporting (optional but recommended)"],"input_types":["Event type (tool invocation, approval, phase transition, etc.)","User identity","Affected artifacts and metadata"],"output_types":["Audit log entries (JSON with timestamps)","Compliance reports","Event summaries"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_2","uri":"capability://automation.workflow.real.time.websocket.based.dashboard.synchronization.across.multiple.projects","name":"real-time websocket-based dashboard synchronization across multiple projects","description":"Operates a Fastify-based HTTP server with WebSocket support that maintains real-time bidirectional communication with browser and VSCode extension clients. The dashboard aggregates state from multiple projects' .spec-workflow/ directories, broadcasts updates via WebSocket when files change (using file system watchers), and provides a unified view of all active projects without requiring clients to poll the file system directly.","intents":["I want to see live updates of workflow progress across all my projects in one dashboard","I need VSCode and browser clients to stay synchronized without polling","I want to monitor multiple projects' approval statuses in real-time"],"best_for":["Teams managing multiple concurrent projects with shared oversight","Developers wanting IDE-integrated workflow visibility","Organizations needing real-time project status dashboards"],"limitations":["File system watchers have platform-specific behavior (inotify on Linux, FSEvents on macOS, ReadDirectoryChangesW on Windows) — rapid file changes may be coalesced or missed","WebSocket connections are stateful and require connection pooling; scaling to 100+ concurrent clients requires horizontal load balancing with shared state (Redis or similar)","Dashboard server is separate from MCP server, requiring two processes to be running for full functionality"],"requires":["Node.js 18+","Port availability for Fastify HTTP server (default 3000)","File system watch capability (inotify/FSEvents/ReadDirectoryChanges)","Browser or VSCode extension client with WebSocket support"],"input_types":["Project directory paths","File system events (create, modify, delete)","WebSocket messages (JSON)"],"output_types":["Real-time WebSocket updates (JSON)","HTML dashboard UI","Project state snapshots"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_3","uri":"capability://tool.use.integration.vscode.extension.sidebar.integration.with.approval.workflow.ui","name":"vscode extension sidebar integration with approval workflow ui","description":"Provides a VSCode extension that renders a sidebar panel connected to the dashboard server via WebSocket, displaying project status, task lists, and an interactive approval workflow interface. The extension allows developers to approve/reject implementations, view specifications, and manage tasks without leaving the editor, with all actions synchronized back to the .spec-workflow/ directory and broadcast to other connected clients.","intents":["I want to approve or reject implementations without switching out of VSCode","I need to see my project's workflow status in the VSCode sidebar while coding","I want task management and approval decisions to be reflected immediately in the dashboard"],"best_for":["VSCode-primary developers who want workflow tools integrated into their editor","Teams where code reviewers and approvers work in VSCode","Projects where approval decisions need to happen in the context of code review"],"limitations":["VSCode extension requires separate installation and configuration beyond the MCP server","Sidebar UI is read-only for some operations (e.g., can view specs but not edit them directly in the sidebar)","Extension communicates with dashboard server, not directly with MCP server — requires both services running","No offline support — sidebar is non-functional if dashboard server is unreachable"],"requires":["VSCode 1.80+","Dashboard server running and accessible (default localhost:3000)","WebSocket connectivity between VSCode and dashboard","Extension installed from marketplace or built from source"],"input_types":["WebSocket messages from dashboard","User interactions (clicks, form submissions)","Project state updates"],"output_types":["Approval/rejection decisions (JSON)","Task status updates","WebSocket messages to dashboard"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_4","uri":"capability://text.generation.language.specification.document.creation.and.version.management.with.template.support","name":"specification document creation and version management with template support","description":"Provides tools for creating and managing specification documents (requirements, design, task lists) stored in the specs/ directory with version tracking. Documents are stored as markdown files with optional JSON frontmatter for metadata, and the system supports templates for consistent structure. The workflow enforces that specifications must be created and approved before dependent phases can proceed, with all versions retained in the archive/ directory.","intents":["I want to create structured requirements and design documents that AI agents can read and build upon","I need to ensure specifications are reviewed and approved before implementation starts","I want to maintain a version history of all specifications for audit and reference"],"best_for":["Teams using AI agents to generate code from specifications","Projects requiring documented requirements before development","Organizations with compliance requirements for design documentation"],"limitations":["Specifications are stored as markdown files — no built-in rich text editing, only plain text/markdown support","Version history is manual (files moved to archive/) — no automatic diff or merge capabilities","No built-in validation of specification completeness or quality — approval is manual","Template system is file-based, not database-backed — scaling to 100+ templates requires careful directory organization"],"requires":["Write access to .spec-workflow/specs/ directory","Markdown editor or IDE for specification authoring","Understanding of specification structure and requirements"],"input_types":["Markdown text","JSON metadata (optional frontmatter)","Template identifiers"],"output_types":["Specification files (markdown with JSON frontmatter)","Archived versions (timestamped copies)","Approval metadata (JSON)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_5","uri":"capability://data.processing.analysis.task.parsing.and.decomposition.from.specifications.into.actionable.work.items","name":"task parsing and decomposition from specifications into actionable work items","description":"Parses specification documents to extract and decompose tasks into a structured task list stored in the tasks/ directory. The system identifies task boundaries (using markdown headers or explicit task markers), extracts dependencies and prerequisites, and generates task metadata (priority, estimated effort, assigned phase). Tasks are stored as JSON objects with references back to source specifications, enabling AI agents to understand the relationship between high-level requirements and concrete work items.","intents":["I want to automatically break down specifications into concrete tasks that AI agents can implement","I need to track task dependencies and ensure prerequisites are completed before dependent tasks start","I want to see which specification each task came from for traceability"],"best_for":["Teams using AI agents for implementation and wanting structured task lists","Projects with complex specifications that need decomposition","Developers wanting automatic task extraction without manual breakdown"],"limitations":["Task parsing relies on consistent markdown structure — poorly formatted specifications may result in missed or incorrectly parsed tasks","Dependency detection is heuristic-based (keyword matching for 'depends on', 'requires', etc.) — complex dependency graphs may not be fully captured","No built-in task estimation — effort and priority must be manually assigned or inferred from specification content","Task parser is TypeScript-only — no Python or other language bindings for custom parsing logic"],"requires":["Specification documents in specs/ directory","Consistent markdown formatting with clear task boundaries","Task parser configured with appropriate extraction rules"],"input_types":["Specification markdown files","Task extraction rules (regex, keyword patterns)"],"output_types":["Task JSON objects with metadata","Dependency graphs","Task lists with references to source specs"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_6","uri":"capability://automation.workflow.implementation.logging.and.artifact.tracking.with.code.reference.binding","name":"implementation logging and artifact tracking with code reference binding","description":"Captures implementation progress by logging code changes, commits, and artifacts as they are created. The system binds implementation artifacts (files, commits, pull requests) to their corresponding tasks and specifications, storing logs in the implementation/ directory with timestamps and metadata. This creates a complete audit trail from specification → task → code, enabling developers to understand why specific code was written and reviewers to trace implementations back to requirements.","intents":["I want to track which code was written to satisfy which task and specification","I need an audit trail showing when implementations were completed and what changed","I want to understand the relationship between requirements, tasks, and the code that was written"],"best_for":["Teams requiring compliance-auditable development (financial, healthcare, regulated industries)","Projects where traceability from requirements to code is critical","Developers wanting to understand the history and rationale behind code changes"],"limitations":["Implementation logging is manual or requires git hook integration — automatic capture requires additional setup","Artifact binding is based on file paths and commit messages — if naming conventions aren't followed, bindings may be incorrect","No built-in integration with pull request systems (GitHub, GitLab) — PR linking requires manual configuration","Storage is file-based, so querying implementation history requires scanning all log files"],"requires":["Write access to .spec-workflow/implementation/ directory","Git repository with commit history (optional but recommended)","Consistent naming conventions for tasks and files"],"input_types":["Code file paths","Git commit hashes and messages","Task identifiers","Implementation metadata (author, timestamp)"],"output_types":["Implementation logs (JSON with timestamps)","Artifact references (file paths, commit hashes)","Traceability reports (spec → task → code)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_7","uri":"capability://automation.workflow.approval.workflow.with.multi.stage.review.and.decision.recording","name":"approval workflow with multi-stage review and decision recording","description":"Implements a structured approval process where implementations are submitted for review, reviewers can approve or request changes, and decisions are recorded with metadata (reviewer identity, timestamp, comments). The approval state is stored in the approvals/ directory as JSON objects, and the system prevents implementations from being marked complete until all required approvals are obtained. The VSCode extension and web dashboard provide UI for reviewers to inspect implementations and make approval decisions.","intents":["I want to ensure implementations are reviewed before being marked complete","I need to track who approved what and when, for compliance and accountability","I want reviewers to be able to request changes and provide feedback without leaving their tools"],"best_for":["Teams with formal code review processes","Organizations requiring documented approval trails","Projects where implementation quality is critical and needs human validation"],"limitations":["Approval workflow is linear (submit → review → approve/reject) — no built-in support for parallel reviews from multiple reviewers","Reviewer assignment is manual — no automatic routing based on expertise or availability","Approval decisions are recorded but not enforced at the code level — a developer could theoretically bypass the approval system by modifying files directly","No built-in notification system — reviewers must actively check the dashboard or VSCode extension to see pending approvals"],"requires":["Write access to .spec-workflow/approvals/ directory","Reviewer identity (username or email) for decision recording","Dashboard or VSCode extension for approval UI"],"input_types":["Implementation artifacts (code, files, commit references)","Reviewer identity and decision (approve/reject)","Review comments (optional)"],"output_types":["Approval decision records (JSON with metadata)","Approval status (pending, approved, rejected)","Review audit logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_8","uri":"capability://automation.workflow.multi.project.registry.and.active.project.tracking","name":"multi-project registry and active project tracking","description":"Maintains a global registry (activeProjects.json in ~/.spec-workflow-mcp/) that tracks all active projects and their metadata (path, status, last updated). The dashboard server reads this registry to discover which projects to monitor, and the MCP server registers itself with the registry when started. This enables the system to aggregate state across multiple projects and provide a unified view without requiring manual project configuration.","intents":["I want the dashboard to automatically discover and monitor all my projects","I need to see status across multiple projects without manually configuring each one","I want to switch between projects in the dashboard without restarting services"],"best_for":["Developers managing multiple concurrent projects","Teams with shared project portfolios","Organizations wanting centralized project visibility"],"limitations":["Registry is file-based (activeProjects.json) — concurrent modifications from multiple MCP server instances can cause race conditions without file locking","Project discovery is passive (projects must be explicitly registered) — no automatic scanning of a projects directory","Registry location is global (~/.spec-workflow-mcp/) — projects on different machines or in sandboxed environments may not be discoverable","No built-in cleanup of stale project entries — projects that are deleted or moved remain in the registry until manually removed"],"requires":["Write access to ~/.spec-workflow-mcp/ directory (or SPEC_WORKFLOW_HOME if overridden)","Project directories with .spec-workflow/ structure","MCP server instances to register themselves on startup"],"input_types":["Project paths","Project metadata (status, last updated)","MCP server process IDs"],"output_types":["activeProjects.json registry file","Project metadata objects","Project discovery results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-pimzino-spec-workflow-mcp__cap_9","uri":"capability://planning.reasoning.interactive.prompt.system.for.ai.agent.guidance.and.decision.support","name":"interactive prompt system for ai agent guidance and decision support","description":"Provides 6+ interactive prompts that guide AI agents through workflow decisions and complex tasks. Prompts are registered as MCP resources that return structured guidance (context, options, examples) to help agents understand what to do next. The system uses prompts to handle ambiguous situations (e.g., 'should I create a new task or extend an existing one?') and to provide domain-specific guidance (e.g., 'how should I structure this specification?').","intents":["I want Claude or Cursor to have guidance on how to structure specifications and tasks","I need the AI agent to ask for clarification when it's unsure about workflow decisions","I want to provide domain-specific examples and best practices to the AI agent"],"best_for":["Teams using AI agents for specification and task creation","Projects where consistent structure and quality are important","Developers wanting to guide AI behavior without fine-tuning models"],"limitations":["Prompts are static resources — they don't adapt based on project context or history","AI agents may ignore or misinterpret prompts if they conflict with model training","No built-in feedback loop to improve prompts based on agent behavior","Prompt content is hardcoded in the MCP server — updating prompts requires restarting the server"],"requires":["MCP-compatible AI client that supports resource prompts","Understanding of prompt engineering and AI agent behavior"],"input_types":["Prompt identifiers","Context parameters (current phase, task, etc.)"],"output_types":["Structured prompt content (markdown with examples)","Decision guidance (options, recommendations)","Best practice examples"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","MCP-compatible client (Claude Desktop, Cursor, or custom MCP host)","Project directory with .spec-workflow/ structure initialized","Project initialized with .spec-workflow/ directory structure","Write access to project root for phase state files","Understanding of the 5-phase workflow model","Write access to project root and home directory (or SPEC_WORKFLOW_HOME)","File system that supports directory creation and file I/O","Understanding of the .spec-workflow/ directory structure","Browser or VSCode with locale settings"],"failure_modes":["Stdio-based transport means single-threaded sequential request handling — concurrent tool calls from multiple agents require separate server instances","TOON format response wrapping adds serialization overhead for large artifact responses","No built-in request queuing or backpressure handling if AI client sends rapid-fire tool calls","Strict sequential enforcement can feel rigid for iterative or exploratory development — no built-in support for phase rollback or parallel work streams","Phase validation is file-system based, so concurrent modifications to phase state files can cause race conditions without external locking","No built-in support for conditional phase skipping (e.g., skipping design for trivial tasks)","File system-based state has no built-in concurrency control — multiple processes writing to the same files can cause corruption without external locking (e.g., flock, Redis)","Querying state requires scanning and parsing files — no indexing or fast lookups like a database would provide","File system performance degrades with large numbers of files (100k+ artifacts) — no built-in sharding or partitioning","Cross-platform file system differences (case sensitivity, path separators) can cause issues when moving projects between Windows and Unix systems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5638231377033358,"quality":0.5,"ecosystem":0.49999999999999994,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-03-07T00:05:39Z"},"community":{"stars":4156,"forks":342,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-pimzino-spec-workflow-mcp","compare_url":"https://unfragile.ai/compare?artifact=mcp-pimzino-spec-workflow-mcp"}},"signature":"sCbuiySpuDEHBVmv2L8YUHFe0B+IuayVO51Wxoy8nQIv2V84Bl6UrFvJZUFBqRoaEoPnhEIwbYV5Q3lxVXylCg==","signedAt":"2026-06-20T23:29:18.434Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-pimzino-spec-workflow-mcp","artifact":"https://unfragile.ai/mcp-pimzino-spec-workflow-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-pimzino-spec-workflow-mcp","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"}}