type: workstream slug: mcp-analyst-agent name: mcp analyst agent description: MCP-based analyst agent tools and AI-assisted workflows. owner: data-ai-engineer-architect status: active milestones: m0-prototype: |- A runnable prototype path exists for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning, with concrete artifacts that prove the flow works end-to-end in the current codebase. Core assumptions are documented, known constraints are explicit, and the team can explain what is real versus mocked without ambiguity. m1-ft-analytics-analyst-pilot: |- Internal analysts can execute at least one weekly real workflow that depends on AI agent tool interfaces, execution workflows, and eval-driven behavior tuning in the 5T Analytics environment, without bespoke engineering intervention for every run. Instrumentation and feedback capture are in place so failures, friction points, and adoption gaps are visible and triaged with owners. m2-internal-adoption-design-partners: |- AI agent tool interfaces, execution workflows, and eval-driven behavior tuning is hardened enough for regular use by multiple internal teams and initial design partners, with a predictable response loop for issues and requests. Quality expectations are documented, and prioritized improvements from real usage are actively incorporated into delivery. m3-public-launch: |- Launch scope for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning is complete, externally explainable, and supportable: user-facing behavior is stable, documentation is publishable, and operational ownership is explicit. Remaining gaps are non-blocking, risk-assessed, and tracked as post-launch follow-up rather than unresolved launch debt. m4-v1-0-launch: |- Post-launch stabilization is complete for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning: recurring incidents are reduced, support burden is lower, and quality gates are enforced consistently before release. The team has a repeatable operating model for maintenance, regression prevention, and measured reliability improvements. m5-v1-2-launch: |- v1.2 delivers meaningful depth improvements in AI agent tool interfaces, execution workflows, and eval-driven behavior tuning based on observed usage and retention signals, not just roadmap intent. Enhancements improve real customer outcomes, and release readiness is demonstrated through metrics, regression coverage, and clear migration guidance where relevant. mx-far-future-ideas: |- Long-horizon opportunities for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning are captured as concrete hypotheses with user impact, prerequisites, and evaluation criteria. Ideas are ranked by strategic value and feasibility so future investment decisions can be made quickly with less rediscovery.
Agent tools and workflows for AI-assisted analysis using Dataface context. This workstream builds the MCP server, tool definitions, and prompt workflows that let AI agents (in Cursor, Claude, etc.) interact with Dataface — inspecting schemas, generating dashboards, running queries, and iterating on analysis. The goal is that an analyst can describe what they want in natural language and an AI agent produces a working dashboard or analysis. Adjacent to inspect-profiler (which provides the data context the agent uses) and context-catalog-nimble (which defines how context is structured and surfaced).
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