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MCP-Native by Design

Memex AI exposes its entire surface as an MCP (Model Context Protocol) server. Any AI agent, regardless of vendor, framework, or runtime, connects to Memex AI and interacts with the strategy/decision/work/blueprint graph through standard tool calls.
This is a deliberate architectural choice. Memex AI is not another AI coding tool. It’s the shared context layer that all AI tools read from and write to.

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Why MCP

MCP is becoming the standard protocol for AI tool integration.

Any AI coding agent

Claude Code, Cursor, GitHub Copilot, custom agents, all connect and access the full strategy/decision/work/blueprint graph.

Multi-vendor, no lock-in

Multiple agents from different vendors share the same context. Swap tools without losing your graph.

Custom agents fit in

Research, testing, deployment, product management. All integrate through the same protocol.

Server is source of truth

Not a file in a repository that might be stale. One place, always current.

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Core Tool Surface

The full MCP surface, grouped by concern. 
list_strategies()                   → all strategies with status summary
get_strategy(id)                    → purpose, vision, decisions, work items
get_strategy_status(id)             → progress overview: open decisions, blocked/ready WIs
promote_work_item(wi_id)            → elevates a WI to its own strategy, preserving lineage

create_strategy_draft(purpose)               → start a new strategy in draft state
update_strategy_draft(id, section, content)  → iterative refinement
get_strategy_draft(id)                       → current state of the draft, formatted for reading
add_draft_decision(id, question, options)    → surface a design choice during planning
add_draft_work_item(id, goal, deps)          → scope a unit of work
publish_strategy(id)                         → move from draft to active; decisions become blockable

get_decision(id)                    → decision with status, options, rationale
get_decisions(strategy_id)          → all decisions for a strategy
create_decision(strategy_id, question, options)
resolve_decision(id, choice, rationale)
reopen_decision(id, reason)         → re-opens a resolved decision, cascades impact
get_decision_impact(id)             → WIs blocked, blueprints affected, cross-strategy deps

get_work_item(id)                   → goal, dependencies, checklist, status
get_ready_work_items(strategy_id?)  → WIs where all decisions resolved + deps met
check_dependencies(wi_id)           → which are met, which block (within and across strategies)
get_dependents(wi_id)               → downstream work this unblocks
update_work_item_status(id, status)
submit_execution_plan(wi_id, plan)
get_execution_plan(wi_id)           → files, dependency flow, conflicts

get_blueprint(domain)                    → full blueprint content
get_blueprints_for_work_item(wi_id)      → which blueprints an agent should load
flag_blueprint_drift(id, evidence)       → "this blueprint says X but code does Y"
get_blueprints_affected_by_decision(id)  → impact analysis before resolving
update_blueprint(id, content, reason)

extract_decisions(session_context)  → candidate decisions from a conversation
create_decision_bundle(decisions[]) → bundle for review
get_pending_bundles(account_id?)    → bundles awaiting review
review_decision(bundle_id, decision_id, action) → approve | reject | flag
approve_bundle(bundle_id)           → approve all decisions in the bundle

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The shape of the integration

Your coding agent connects to the Memex AI MCP server at the start of a session. From that point it can list strategies, fetch decisions, check readiness, produce execution plans, and report drift, all through standard MCP tool calls. No custom plugin. No vendor-specific SDK. The agent you’re already using gets the strategy/decision/work/blueprint graph for free.