insight-blueprint
Allows export of data lineage diagrams in Mermaid format for visualizing data transformations.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@insight-blueprint create a new analysis design for customer churn"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
insight-blueprint
A Python MCP server for hypothesis-driven data analysis. Manage analysis designs, data catalogs, and review workflows through Claude Code or any MCP-compatible client.
Installation
Recommended: Claude Code Plugin
# Option 1: From the official marketplace
claude plugin install etoyama/insight-blueprint
# Option 2: Via custom marketplace (permanent install)
/plugin marketplace add etoyama/insight-blueprint
/plugin install insight-blueprint@insight-blueprint-marketplace
# Option 3: From a local clone (session only)
git clone https://github.com/etoyama/insight-blueprint.git
claude --plugin-dir ./insight-blueprintAll options provide 8 analysis skills and auto-configure the MCP server. A WebUI dashboard opens automatically at http://127.0.0.1:3000.
Tip: Option 3 loads the plugin for the current session only. Add a shell alias for convenience:
alias claude-ib='claude --plugin-dir /path/to/insight-blueprint'
Alternative: Direct Execution
# Start the server without plugin (zero-install)
uvx insight-blueprint --project /path/to/my-analysis
# Or install permanently
uv tool install insight-blueprint
insight-blueprint --project /path/to/my-analysisUpdating
When a new version is published, run the following from within Claude Code to pull the latest plugin (auto-update is off by default for third-party marketplaces):
/plugin marketplace update insight-blueprint-marketplace
/plugin update insight-blueprint@insight-blueprint-marketplaceSee CHANGELOG.md for release notes.
Optional: Python Package
For data-lineage tracking with tracked_pipe in your notebooks/scripts:
uv add insight-blueprintThis is optional but recommended for analysis pipeline transparency. MCP tools work without it.
Related MCP server: Dataiku DSS MCP Server
Features
MCP Tools
insight-blueprint exposes 18 tools via the Model Context Protocol, allowing AI assistants to manage your analysis workflow:
Category | Tools |
Analysis Design |
|
Data Catalog |
|
Domain Knowledge |
|
Review Workflow |
|
Project |
|
WebUI Dashboard
A browser-based dashboard (http://127.0.0.1:3000) with two tabs:
Designs -- Browse analysis designs, view details (overview + history), and track status transitions
Catalog -- Search domain knowledge, browse data sources, and check cautions
Bundled Skills
The plugin provides 10 analysis skills that are automatically available after installation:
/rq-problematization-- Generate impactful research questions by problematizing the assumptions in prior research (upstream of framing)/analysis-framing-- Explore available data and existing analyses to frame a hypothesis direction/analysis-design-- Guided workflow for creating hypothesis documents/analysis-journal-- Record reasoning steps during analysis (observations, evidence, decisions, questions)/analysis-reflection-- Structured reflection to draw conclusions or branch hypotheses/analysis-revision-- Guided revision workflow for addressing review comments/catalog-register-- Step-by-step data source registration/data-lineage-- Track data transformations and export lineage diagrams (Mermaid)/batch-analysis-- Overnight batch execution of queued designs (headless notebooks, self-review, journal recording)/premortem-- Pre-flight risk evaluation of queued designs with approval token issuance (gates/batch-analysis)
Skills support both English and Japanese trigger phrases.
Analysis Workflow
Skills chain together to support the full hypothesis-driven analysis lifecycle:
/rq-problematization (problematize assumptions → research questions) ← optional upstream
↓ (RQ Brief)
/analysis-framing (explore data, frame direction)
↓
/analysis-design (create hypothesis)
↓ (interactive) ↓ (batch)
/analysis-journal /batch-analysis (overnight headless)
↓ ↓
↓
/analysis-reflection (reflect → conclude or branch) ← morning review
↓ ↗ back to /analysis-framing (new direction needed)
↕ WebUI review → /analysis-revision (address review comments)
/catalog-register (register findings as domain knowledge)Each design has an analysis_intent field (exploratory, confirmatory, or mixed) to distinguish whether you're testing a specific hypothesis or exploring data for patterns. The Insight Journal (.insight/designs/{id}_journal.yaml) tracks your reasoning process with 8 event types mapped to the Narrative Scaffolding framework (Huang+ IUI 2026).
Overnight Operation
Batch analysis runs overnight via a two-step workflow: risk evaluation followed by headless execution.
Workflow
/premortem --queued --yes --mode review
↓ (exit 0: token issued)
↓ (exit 2: HIGH detected, human triage needed)
/batch-analysis --approved-by TOKEN
↓
Morning review: summary.md + /analysis-reflection per designAutomation Modes
Mode | HIGH Risk Handling | Human Interaction |
| Interactive prompt for every design | Required |
| Blocks on HIGH (exit 2), auto-approves LOW/MEDIUM | Only when HIGH detected |
| Includes HIGH in approved set with warning | None |
Set the mode in .insight/config.yaml under batch.automation (default: review).
Phased Rollout of --approved-by
The --approved-by TOKEN argument is introduced in two phases:
Phase A (
batch.approved_by_required: false): Omitting the flag prints a warning and runs in legacy mode. Existing workflows are not broken.Phase B (
batch.approved_by_required: true): Omitting the flag causes exit 1. All batch runs must go through/premortemfirst.
Transition from Phase A to Phase B by setting approved_by_required: true in
.insight/config.yaml when your team is ready.
CLI Options
insight-blueprint --project /path/to/project # Specify project directory
insight-blueprint --no-browser # Suppress browser auto-open
insight-blueprint --version # Show version
insight-blueprint # Use current directoryTeam Server Mode
Multiple Claude Code instances can share a single insight-blueprint server via MCP SSE (Server-Sent Events).
Server mode (WebUI + MCP SSE)
insight-blueprint --project /path/to/project --mode server --port 4000Each Claude Code instance connects by adding to .claude/settings.json:
{
"mcpServers": {
"insight-blueprint": {
"type": "sse",
"url": "http://<host>:4000/mcp/sse"
}
}
}Headless mode (MCP SSE only, no WebUI)
insight-blueprint --project /path/to/project --mode headless --port 4000Options
Option | Default | Description |
| (default) | stdio MCP + WebUI on localhost:3000. Standard single-user mode |
| - | HTTP MCP SSE + WebUI on the same port. For team/multi-client use |
| - | HTTP MCP SSE only (no WebUI). Lightweight deployment |
|
| Bind address (server/headless mode only) |
|
| Listen port (server/headless mode only) |
|
| Suppress browser auto-open in full mode |
WARNING: No authentication. Phase 1 does not include authentication. Run the server on a trusted network only, or bind to localhost with
--host 127.0.0.1.
Migration Guide (from v0.3.x)
If you previously used insight-blueprint without the plugin system, clean up the old skill copies:
# Remove old skill copies (now provided by the plugin)
rm -rf .claude/skills/analysis-design .claude/skills/analysis-framing \
.claude/skills/analysis-journal .claude/skills/analysis-reflection \
.claude/skills/analysis-revision .claude/skills/catalog-register \
.claude/skills/data-lineage
# Remove old rule copies (now integrated into skill definitions)
rm -rf .claude/rules/analysis-workflow.md .claude/rules/catalog-workflow.md \
.claude/rules/insight-yaml.md .claude/rules/extension-policy.mdThe plugin's skills take precedence, so old copies won't cause errors but should be removed to avoid confusion.
Development
Requires Python 3.11+, uv, and Node.js (for frontend build).
git clone https://github.com/etoyama/insight-blueprint.git
cd insight-blueprint
uv sync --all-extras
# Build frontend assets (required for WebUI)
poe build-frontend
# Run lint + typecheck + test
poe allSee CONTRIBUTING.md for setup instructions, code style, and how to submit pull requests.
Tech Stack
Tool | Purpose |
uv | Package management |
ruff | Linting and formatting |
ty | Type checking |
pytest | Testing |
FastMCP | MCP server framework |
FastAPI | WebUI backend |
Support
If you find this project useful, consider buying me a coffee.
License
MIT
Available Tools
18 toolsadd_catalog_entryC
Register a new data source in the catalog.
Inputs: name (required), type (required), source_id (required), connection (required), +5 more. Returns: object
create_analysis_designA
Create a new analysis design document.
Creates a YAML file in .insight/designs/ with 'in_review' status. theme_id must match [A-Z][A-Z0-9]* pattern (e.g., 'FP', 'TX', 'DEFAULT').
methodology: Analysis method dict with required 'method' key. Example: {"method": "OLS", "package": "statsmodels", "reason": "..."} WARNING: Should rarely be None — methodology is a core design field.
Returns: dict with id, title, status, message
Inputs: title (required), hypothesis_statement (required), hypothesis_background (required), chart, +8 more. Returns: object
extract_domain_knowledgeA
Extract domain knowledge from review comments as preview.
Returns extracted entries for user review before persistence. Call save_extracted_knowledge() to persist confirmed entries.
Returns: dict with design_id, entries, count, message
Inputs: design_id (required). Returns: object
get_analysis_designA
Retrieve an analysis design by ID.
Returns the full design as a dict, or an error dict if not found.
Inputs: design_id (required). Returns: object
get_domain_knowledgeC
Get domain knowledge entries for a data source.
Inputs: source_id (required), category. Returns: object
get_project_contextA
Get aggregated project context including all domain knowledge.
Returns sources, knowledge entries, rules, and counts from both catalog and review-extracted knowledge.
Inputs: no named parameters. Returns: object
get_review_commentsA
Get review comments for a design.
Returns all review batches sorted by created_at descending (newest first). Returns empty list if no reviews exist or file is corrupted.
Inputs: design_id (required). Returns: object
get_table_schemaA
Get the column schema for a data source.
Inputs: source_id (required). Returns: object
list_analysis_designsA
List all analysis designs, optionally filtered by status.
Args: status: Optional filter (in_review|revision_requested|analyzing|supported|rejected|inconclusive)
Returns: dict with 'designs' list and 'count' integer
Inputs: status. Returns: object
save_extracted_knowledgeA
Persist user-confirmed knowledge entries to extracted_knowledge.yaml.
Call extract_domain_knowledge() first to get preview entries, then pass confirmed (optionally adjusted) entries here.
Args: design_id: The design ID the entries were extracted from entries: List of dicts with keys: key, content, category, affects_columns
Returns: dict with design_id, saved_entries, count, message
Inputs: entries (required), design_id (required). Returns: object
save_review_batchA
Save a batch of review comments and transition the design status.
The design must be in reviewable status (in_review or revision_requested). Each comment can optionally include target_section and target_content for inline anchoring.
Valid status_after values: revision_requested, analyzing, supported, rejected, inconclusive.
Returns: dict with batch_id and status_after on success; {error} on failure
Inputs: comments (required), design_id (required), status_after (required), reviewer. Returns: object
save_review_commentA
Save a review comment and transition the design status.
The design must be in reviewable status (in_review or revision_requested). Valid post-review statuses: revision_requested, analyzing, supported, rejected, inconclusive.
Returns: dict with comment_id, design_id, status_after, message
Inputs: status (required), comment (required), design_id (required), reviewer. Returns: object
search_catalogC
Search the data catalog using full-text search.
Inputs: query (required), tags, source_type. Returns: object
suggest_cautionsA
Suggest cautions for the given table/source names.
Searches all domain knowledge entries (catalog and extracted) by matching affects_columns against provided table names.
Args: table_names: Comma-separated string of table/source names
Returns: dict with table_names, cautions, count
Inputs: table_names (required). Returns: object
suggest_knowledge_for_designA
Suggest knowledge entries relevant to a design section.
Filters by category via SECTION_KNOWLEDGE_MAP, then applies per-category matching strategies (theme_id, source_ids, FTS5, lineage).
Args: section: Design section name (e.g., hypothesis_statement, metrics) theme_id: Theme ID to match findings/context by source_ids: Comma-separated source IDs for caution/definition matching hypothesis_text: Text to search via FTS5 for methodology matching parent_id: Design ID to walk ancestor chain for finding collection
Returns: dict with section, suggestions, total
Inputs: section, theme_id, parent_id, source_ids, +1 more. Returns: object
transition_design_statusA
Transition a design to the given target status.
Valid transitions depend on the current status:
in_review -> revision_requested, analyzing, supported, rejected, inconclusive
revision_requested -> in_review
analyzing -> in_review
supported, rejected, inconclusive -> (terminal, no transitions)
Returns: dict with design_id, status on success; {error} on failure
Inputs: status (required), design_id (required). Returns: object
update_analysis_designA
Partially update an existing analysis design.
Only provided fields are updated. Returns the updated design as a dict, or an error dict if design_id not found. Status changes must go through transition_design_status.
Inputs: design_id (required), chart, title, metrics, +7 more. Returns: object
update_catalog_entryC
Update an existing data source in the catalog.
Inputs: source_id (required), name, tags, columns, +2 more. Returns: object
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 18 tools
Most tools have clear single purposes (get vs list, create vs update), but there is overlap between transition_design_status and save_review_comment/save_review_batch, which also transition status. The suggestion tools (suggest_cautions vs suggest_knowledge_for_design) are distinct in inputs but may be confused in intent.
All tool names follow a predictable verb_noun pattern with snake_case (get_*), (create_*), (update_*), (save_*), etc. The convention is uniform and makes it easy to infer functionality.
18 tools is on the higher end but appropriate given the server covers analysis design lifecycle, catalog management, review workflows, and knowledge extraction. Each tool serves a distinct functional area, so the count feels justified rather than bloated.
Core workflows for design creation, review, and knowledge extraction are well covered. However, there are no delete operations (e.g., delete_analysis_design, delete_catalog_entry) and no direct way to add domain knowledge manually. These gaps may require workarounds in agent workflows.
Maintenance
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