{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-squad-ai","slug":"squad-ai","name":"Squad AI","type":"mcp","url":"https://github.com/the-basilisk-ai/squad-mcp","page_url":"https://unfragile.ai/squad-ai","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-squad-ai__cap_0","uri":"capability://tool.use.integration.mcp.native.opportunity.creation.and.management","name":"mcp-native opportunity creation and management","description":"Enables LLM agents to create, read, update, and delete opportunity records through standardized MCP protocol handlers that expose CRUD operations as callable tools. Implements a schema-based resource model where opportunities are structured entities with metadata fields (title, description, status, owner) that persist in an underlying data store, allowing multi-turn conversations to reference and modify opportunities without re-specification.","intents":["I want my Claude/LLM agent to create new product opportunities from conversation context without manual data entry","I need to query existing opportunities by criteria (status, owner, date range) and have the LLM reason over results","I want to update opportunity details (pivot scope, change status) through natural language commands executed by the agent"],"best_for":["Product managers building AI-assisted discovery workflows","Teams using Claude or other MCP-compatible LLMs as strategy assistants","Organizations wanting to embed opportunity tracking into agent-driven processes"],"limitations":["No built-in access control — all MCP-connected agents have equal write permissions to opportunities","Persistence mechanism not specified in documentation — may require external database configuration","No transaction support for multi-step opportunity updates, risking inconsistent state if agent fails mid-operation"],"requires":["MCP-compatible LLM client (Claude, or other MCP-aware model)","Node.js runtime for MCP server execution","Underlying data store (implementation-dependent, likely SQLite or PostgreSQL)"],"input_types":["natural language descriptions","structured JSON opportunity objects","query filters (status, owner, date range)"],"output_types":["structured JSON opportunity records","query result sets","operation confirmation messages"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-squad-ai__cap_1","uri":"capability://tool.use.integration.solution.to.opportunity.linking.and.relationship.mapping","name":"solution-to-opportunity linking and relationship mapping","description":"Provides MCP tools to establish and query relationships between solutions and opportunities, enabling agents to map which solutions address which opportunities and vice versa. Implements a graph-like relationship model where solutions can be associated with multiple opportunities and opportunities can reference multiple solutions, with the agent able to traverse these relationships to identify coverage gaps or solution redundancy.","intents":["I want the agent to suggest which existing solutions could address a newly identified opportunity","I need to find all opportunities that a particular solution could solve","I want to identify opportunities with no mapped solutions to prioritize new solution development"],"best_for":["Product strategy teams managing solution portfolios","Organizations with complex opportunity-to-solution mappings requiring AI-assisted analysis","Teams building agent-driven product roadmap planning"],"limitations":["Relationship cardinality not specified — unclear if many-to-many relationships are fully supported or if there are constraints","No built-in conflict detection — agent may create circular or contradictory solution-opportunity mappings","Relationship metadata (confidence scores, coverage percentage) not mentioned, limiting nuanced analysis"],"requires":["MCP-compatible LLM client","Existing opportunity and solution records in the data store","Node.js MCP server with relationship persistence layer"],"input_types":["opportunity IDs","solution IDs","relationship metadata (optional)"],"output_types":["relationship records","opportunity-solution mapping lists","coverage analysis results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-squad-ai__cap_2","uri":"capability://data.processing.analysis.outcome.and.requirement.extraction.from.opportunity.context","name":"outcome and requirement extraction from opportunity context","description":"Enables agents to parse opportunity descriptions and automatically extract structured outcomes (desired end states) and requirements (constraints, dependencies, success criteria) using LLM reasoning. Implements a schema-based extraction pattern where the agent analyzes free-form opportunity text and populates outcome and requirement records with semantic understanding, allowing downstream tools to operate on structured data rather than unstructured descriptions.","intents":["I want the agent to automatically identify success metrics and desired outcomes from a verbose opportunity description","I need to extract technical and business requirements from opportunity context to inform solution design","I want to ensure all opportunities have clearly defined outcomes and requirements before moving to solution phase"],"best_for":["Product teams with high-volume opportunity intake lacking structured data","Organizations building AI-assisted requirements gathering workflows","Teams wanting to enforce outcome/requirement documentation standards via agent automation"],"limitations":["Extraction accuracy depends on LLM capability — ambiguous or poorly-written opportunity descriptions may produce incomplete or incorrect outcomes/requirements","No validation schema enforcement — extracted requirements may violate business rules or conflict with existing requirements","Extraction is one-directional (opportunity → outcomes/requirements) with no feedback loop to refine extraction quality"],"requires":["MCP-compatible LLM with strong semantic understanding","Opportunity records with descriptive text fields","Outcome and requirement schema definitions"],"input_types":["opportunity descriptions (natural language text)","optional extraction templates or examples"],"output_types":["structured outcome records","structured requirement records","extraction confidence metadata (if supported)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-squad-ai__cap_3","uri":"capability://tool.use.integration.feedback.collection.and.opportunity.refinement.loops","name":"feedback collection and opportunity refinement loops","description":"Provides MCP tools for agents to collect structured feedback on opportunities, solutions, and outcomes, then use that feedback to iteratively refine opportunity definitions. Implements a feedback schema where stakeholder input (approval, concerns, suggested changes) is captured as structured records linked to opportunities, enabling agents to aggregate feedback and suggest modifications to opportunity scope, requirements, or success criteria.","intents":["I want the agent to collect stakeholder feedback on proposed opportunities and summarize concerns","I need to track which opportunities have been reviewed and approved vs. those requiring revision","I want the agent to suggest opportunity refinements based on aggregated feedback without manual synthesis"],"best_for":["Organizations with distributed stakeholder review processes","Teams building AI-assisted product governance workflows","Companies wanting to automate opportunity validation and approval cycles"],"limitations":["Feedback aggregation logic not specified — unclear how agent prioritizes conflicting feedback from multiple stakeholders","No built-in stakeholder role/permission model — cannot distinguish feedback from domain experts vs. casual reviewers","Feedback loop closure mechanism undefined — agent may suggest refinements but lack authority to implement or escalate decisions"],"requires":["MCP-compatible LLM client","Opportunity records to collect feedback on","Feedback schema with structured fields (feedback type, stakeholder, content, timestamp)"],"input_types":["feedback text (natural language)","structured feedback metadata (stakeholder ID, feedback category)","opportunity IDs for feedback linkage"],"output_types":["feedback records","feedback aggregation summaries","refinement suggestions","approval/rejection status updates"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-squad-ai__cap_4","uri":"capability://tool.use.integration.multi.llm.opportunity.querying.and.cross.model.consensus","name":"multi-llm opportunity querying and cross-model consensus","description":"Enables multiple MCP-compatible LLM clients to query the same opportunity data store and reason over opportunities independently, with optional consensus mechanisms where agents compare analyses and flag disagreements. Implements a shared data model where opportunities are version-controlled and queryable by any connected agent, allowing different LLM models (Claude, GPT, open-source) to analyze the same opportunity set and surface divergent interpretations for human review.","intents":["I want to run the same opportunity analysis through multiple LLM models and compare their recommendations","I need to identify opportunities where different AI agents disagree on priority or feasibility to flag for human review","I want to leverage different LLM strengths (e.g., Claude for strategy, GPT for technical analysis) on the same opportunity set"],"best_for":["Organizations wanting to reduce single-model bias in AI-assisted product decisions","Teams building multi-agent systems with cross-validation requirements","Companies with heterogeneous LLM infrastructure (multiple model providers)"],"limitations":["Consensus algorithm not specified — unclear how agent disagreements are weighted or resolved","No built-in cost optimization — querying multiple LLMs for the same analysis multiplies API costs","Latency increases with number of models queried — no parallel execution guarantees specified"],"requires":["Multiple MCP-compatible LLM clients with API credentials","Shared opportunity data store accessible to all connected agents","Consensus/comparison logic (custom implementation or framework-provided)"],"input_types":["opportunity IDs or query filters","analysis prompts or reasoning templates"],"output_types":["per-model analysis results","consensus summaries","disagreement flags with reasoning"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-squad-ai__cap_5","uri":"capability://planning.reasoning.opportunity.prioritization.and.roadmap.generation","name":"opportunity prioritization and roadmap generation","description":"Provides MCP tools for agents to analyze opportunities against weighted criteria (business impact, effort, risk, strategic alignment) and generate prioritized opportunity lists or product roadmaps. Implements a scoring model where opportunities are evaluated across multiple dimensions and ranked, with agents able to adjust weights, add custom criteria, and generate alternative prioritization scenarios for comparison.","intents":["I want the agent to rank opportunities by impact-to-effort ratio and suggest which to pursue first","I need to generate multiple prioritization scenarios (aggressive growth vs. risk-averse) and compare outcomes","I want to ensure opportunity prioritization aligns with strategic goals by weighting criteria accordingly"],"best_for":["Product leadership teams making roadmap decisions","Organizations wanting AI-assisted prioritization to reduce decision bias","Teams building scenario-planning workflows"],"limitations":["Scoring criteria and weights are subjective — agent recommendations only as good as the criteria provided","No built-in sensitivity analysis — unclear how agent handles uncertainty in impact/effort estimates","Roadmap generation assumes linear execution — no dependency modeling or parallel opportunity tracking"],"requires":["MCP-compatible LLM client","Opportunity records with impact, effort, risk, and alignment metadata","Prioritization criteria and weighting schema"],"input_types":["opportunity records with scoring metadata","prioritization criteria and weights","optional constraints (budget, timeline, team capacity)"],"output_types":["ranked opportunity lists","roadmap timelines","prioritization rationale","scenario comparison reports"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-squad-ai__cap_6","uri":"capability://tool.use.integration.batch.import.and.export.of.product.discovery.data","name":"batch import and export of product discovery data","description":"Provides MCP tools to import product discovery data from external formats (CSV, JSON, YAML) and export the current database state for backup, migration, or integration with external tools. Implements schema validation during import to ensure data integrity and supports partial imports (e.g., opportunities only) for incremental data loading.","intents":["Import existing opportunities and solutions from a spreadsheet or legacy system","Export the current product strategy as JSON for version control or sharing","Migrate data between Squad AI instances or to external product management tools","Create backups of the product discovery database"],"best_for":["Teams migrating from spreadsheet-based product discovery to LLM-driven workflows","Organizations integrating Squad AI with existing product management systems","Builders creating data pipelines for product discovery"],"limitations":["No built-in data transformation — import format must match Squad AI schema exactly; no field mapping or normalization","Import validation is schema-only; no semantic validation (e.g., checking for duplicate opportunities)","Export is full-database only; no selective export (e.g., opportunities by status)","No streaming support — large imports (>100k records) may timeout or consume excessive memory"],"requires":["MCP-compatible LLM client","Persistent backend storage","Node.js 18+ or Python 3.9+","Input data in CSV, JSON, or YAML format matching Squad AI schema"],"input_types":["CSV, JSON, or YAML files with opportunity, solution, outcome, requirement, or feedback records","Import options (merge vs. replace, validation strictness)"],"output_types":["Import confirmation (records imported, validation errors)","Export file (JSON or CSV with full database state)","Migration report (records processed, conflicts, warnings)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible LLM client (Claude, or other MCP-aware model)","Node.js runtime for MCP server execution","Underlying data store (implementation-dependent, likely SQLite or PostgreSQL)","MCP-compatible LLM client","Existing opportunity and solution records in the data store","Node.js MCP server with relationship persistence layer","MCP-compatible LLM with strong semantic understanding","Opportunity records with descriptive text fields","Outcome and requirement schema definitions","Opportunity records to collect feedback on"],"failure_modes":["No built-in access control — all MCP-connected agents have equal write permissions to opportunities","Persistence mechanism not specified in documentation — may require external database configuration","No transaction support for multi-step opportunity updates, risking inconsistent state if agent fails mid-operation","Relationship cardinality not specified — unclear if many-to-many relationships are fully supported or if there are constraints","No built-in conflict detection — agent may create circular or contradictory solution-opportunity mappings","Relationship metadata (confidence scores, coverage percentage) not mentioned, limiting nuanced analysis","Extraction accuracy depends on LLM capability — ambiguous or poorly-written opportunity descriptions may produce incomplete or incorrect outcomes/requirements","No validation schema enforcement — extracted requirements may violate business rules or conflict with existing requirements","Extraction is one-directional (opportunity → outcomes/requirements) with no feedback loop to refine extraction quality","Feedback aggregation logic not specified — unclear how agent prioritizes conflicting feedback from multiple stakeholders","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=squad-ai","compare_url":"https://unfragile.ai/compare?artifact=squad-ai"}},"signature":"i+k31xAyO49XJsfiY4ggjz1B7tXbVYIwXDXmC7yXCghaB5u/Z8BwdbdgfKvmrhu+fAyZrpGp/FbUGlh0NdZ0Aw==","signedAt":"2026-06-21T23:46:21.448Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/squad-ai","artifact":"https://unfragile.ai/squad-ai","verify":"https://unfragile.ai/api/v1/verify?slug=squad-ai","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"}}