ContextStream MCP Server

Initialize a new conversation session and automatically retrieve relevant context. This is the FIRST tool AI assistants should call when starting a conversation. Returns: workspace info, project info, recent memory, recent decisions, relevant context, high-priority lessons, and ingest_recommendation.

The ingest_recommendation field indicates if the project needs indexing for code search:

• If [INGEST_RECOMMENDED] appears, ask the user if they want to enable semantic code search

• Benefits: AI-powered code understanding, dependency analysis, better context retrieval

• If user agrees, run: project(action="ingest_local", path="<project_path>")

IMPORTANT: Pass the user's FIRST MESSAGE as context_hint to get semantically relevant context! Example: init(folder_path="/path/to/project", context_hint="how do I implement auth?")

This does semantic search on the first message. You only need context on subsequent messages.

Generate AI rule files for editors (Cursor, Cline, Kilo Code, Roo Code, Claude Code, Aider). Defaults to the current project folder; no folder_path required when run from a project. Supported editors: codex, cursor, cline, kilo, roo, claude, aider, antigravity

CALL THIS BEFORE EVERY AI RESPONSE to get relevant context.

This is the KEY tool for token-efficient AI interactions. It:

1. Analyzes the user's message to understand what context is needed

2. Retrieves only relevant context in a minified, token-efficient format

3. Replaces the need to include full chat history in prompts

Format options:

• 'minified': Ultra-compact D:decision|P:preference|M:memory (default, ~200 tokens)

• 'readable': Line-separated with labels

• 'structured': JSON-like grouped format

Type codes: W=Workspace, P=Project, D=Decision, M=Memory, I=Insight, T=Task, L=Lesson

Context Pack:

• mode='pack' adds code context + distillation (higher credit cost)

Example usage:

1. User asks "how should I implement auth?"

2. AI calls context(user_message="how should I implement auth?")

3. Gets: "W:Maker|P:contextstream|D:Use JWT for auth|D:No session cookies|M:Auth API at /auth/..."

4. AI responds with relevant context already loaded

This saves ~80% tokens compared to including full chat history.

Search workspace memory and knowledge. Modes: auto (recommended), semantic (meaning-based), hybrid (legacy alias for auto), keyword (exact match), pattern (regex), exhaustive (all matches like grep), refactor (word-boundary matching for symbol renaming), team (cross-project team search - team plans only).

Output formats: full (default, includes content), paths (file paths only - 80% token savings), minimal (compact - 60% savings), count (match counts only - 90% savings).

Session management operations. Actions: capture (save decision/insight), capture_lesson (save lesson from mistake), get_lessons (retrieve lessons), recall (natural language recall), remember (quick save), user_context (get preferences), summary (workspace summary), compress (compress chat), delta (changes since timestamp), smart_search (context-enriched search), decision_trace (trace decision provenance), restore_context (restore state after compaction). Plan actions: capture_plan (save implementation plan), get_plan (retrieve plan with tasks), update_plan (modify plan), list_plans (list all plans). Suggested rules actions: list_suggested_rules (view ML-generated rule suggestions), suggested_rule_action (accept/reject/modify a suggestion), suggested_rules_stats (view ML accuracy stats). Team actions (team plans only): team_decisions (team-wide decisions), team_lessons (team-wide lessons), team_plans (plans across team workspaces).

Memory operations for events and nodes. Event actions: create_event, get_event, update_event, delete_event, list_events, distill_event, import_batch (bulk import array of events). Node actions: create_node, get_node, update_node, delete_node, list_nodes, supersede_node. Query actions: search, decisions, timeline, summary. Task actions: create_task (create task, optionally linked to plan), get_task, update_task (can link/unlink task to plan via plan_id), delete_task, list_tasks, reorder_tasks. Todo actions: create_todo, list_todos, get_todo, update_todo, delete_todo, complete_todo. Diagram actions: create_diagram, list_diagrams, get_diagram, update_diagram, delete_diagram. Doc actions: create_doc, list_docs, get_doc, update_doc, delete_doc, create_roadmap. Transcript actions: list_transcripts (list saved conversations), get_transcript (get full transcript by ID), search_transcripts (semantic search across conversations), delete_transcript. Team actions (team plans only): team_tasks, team_todos, team_diagrams, team_docs.

Code graph analysis. Actions: dependencies (module deps), impact (change impact), call_path (function call path), related (related nodes), path (path between nodes), decisions (decision history), ingest (build graph), circular_dependencies, unused_code, contradictions.

Project management. Actions: list, get, create, update, index (trigger indexing), overview, statistics, files, index_status, index_history (audit trail of indexed files), ingest_local (index local folder), team_projects (list all team projects - team plans only).

Workspace management. Actions: list, get, associate (link folder to workspace), bootstrap (create workspace and initialize), team_members (list members with access - team plans only), index_settings (get/update multi-machine sync settings - admin only).

Reminder management. Actions: list, active (pending/overdue), create, snooze, complete, dismiss.

Utility and help. Actions: tools (list available tools), auth (current user), version (server version), editor_rules (generate AI editor rules and install hooks for real-time file indexing), enable_bundle (enable tool bundle in progressive mode), team_status (team subscription info - team plans only).