Squad AI

Exposes create, read, update, and delete operations for product-discovery opportunities through the Model Context Protocol (MCP) interface, enabling any MCP-aware LLM to directly manipulate opportunity records without custom API client code. Implements standard MCP resource handlers that serialize/deserialize opportunity objects to JSON, with support for filtering and pagination through query parameters passed via MCP tool invocations.

Provides MCP tools to create, query, and update solution records that map to opportunities, enabling LLMs to propose and iterate on product solutions within the discovery workflow. Solutions are linked to parent opportunities and track design decisions, trade-offs, and implementation notes as structured JSON documents that LLMs can read and modify.

Exposes MCP tools to define, query, and update success outcomes for opportunities and solutions, enabling LLMs to establish measurable goals and track progress toward product-discovery milestones. Outcomes are stored as structured records with target metrics, success criteria, and status, allowing LLMs to reason about whether a solution achieves its intended outcomes.

Provides MCP tools to create, query, and update product requirements linked to opportunities and solutions, enabling LLMs to extract and organize requirements from natural language descriptions and user feedback. Requirements are stored as structured records with priority, status, and traceability links, allowing LLMs to reason about requirement coverage and conflicts.

Exposes MCP tools to capture, query, and organize feedback records linked to opportunities and solutions, enabling LLMs to aggregate stakeholder input and synthesize insights. Feedback is stored as structured records with source, sentiment, and category, allowing LLMs to identify patterns and inform product decisions.

Enables multiple LLM agents to collaborate on product discovery by sharing access to the same MCP server and opportunity/solution/outcome/requirement/feedback resources. Each agent can read and write to shared resources, with eventual consistency semantics and no built-in locking or conflict resolution. Agents coordinate through the shared data model rather than direct communication.

Orchestrates multi-step product discovery workflows by exposing MCP tools that LLMs can invoke in sequence to create opportunities, propose solutions, define outcomes, capture requirements, and synthesize feedback. Workflows are implicit in the LLM's reasoning and action sequence rather than explicitly defined, enabling flexible, conversational discovery processes.