Dataface Tasks
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    Future opportunity research

    IDMX_FAR_FUTURE_IDEAS-MCP_ANALYST_AGENT-01
    Statusnot_started
    Priorityp3
    Milestonemx-far-future-ideas
    Ownerdata-ai-engineer-architect

    Problem

    The MCP server roadmap beyond v1.2 is blank. Potential high-impact capabilities — multi-agent collaboration, natural-language-to-dashboard pipelines, semantic layer integration, cross-dashboard analysis, scheduled agent workflows — are discussed informally but not captured in a structured way that evaluates user impact, strategic fit, and technical feasibility. Without a research backlog, long-horizon planning is reactive (driven by whatever the loudest request is) rather than strategic, and the team risks missing opportunities that require long lead times to build.

    Context

    • There are plausible future bets for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning, but they should be captured as concrete opportunities with user value and strategic fit rather than as loose brainstorming.
    • This task should separate genuinely promising directions from attractive-but-vague ideas and tie each candidate to the product or platform outcomes it could improve.
    • Expected touchpoints include dataface/ai/, MCP/tool contracts, cloud chat surfaces, eval runners, and prompt artifacts, roadmap notes, adjacent workstream dependencies, and any evidence from users that points toward a longer-horizon opportunity.

    Possible Solutions

    • A - Keep future ideas as an unstructured backlog list: easy to collect, but hard to prioritize or revisit intelligently.
    • B - Recommended: turn future ideas into structured opportunity notes: describe user value, strategic rationale, dependencies, and why each idea is worth considering later.
    • C - Skip future opportunity work until all near-term milestones are complete: lowers distraction, but loses useful strategic context.

    Plan

    1. Gather the strongest future ideas for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning from roadmap discussions, usage feedback, and adjacent workstream needs.
    2. Write a structured note for each opportunity covering user value, likely scope, dependencies, and the signals that would justify investment.
    3. Rank the opportunities relative to each other and explicitly note which ones are interesting but not yet credible.
    4. Link the top opportunities to prerequisite or experiment tasks so they can mature without being mistaken for near-term commitments.

    Implementation Progress

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