Meaningful Chunker — Code Intelligence API

A graph-based code analysis system that scans any codebase once, builds a semantic graph of all components and their relationships, then answers natural-language queries against that graph. Returns structured, ranked results with explanation — not raw file dumps.

🖥️ No Code Required — Try It in Your Browser

Open meaningful-chunker-production.up.railway.app, get a free key, paste a GitHub URL, and start asking questions. The web UI shows the same results as the API — rendered as human-readable cards with tier labels, relevance scores, change risk, and blast radius.

The API documentation below is for developers integrating the scanner into agents or pipelines.

⚠️ Required First Step

You MUST scan a codebase before using any query endpoints.

If you skip this, all queries will fail with a no_scan_loaded error.

Always start with POST /scan. See the Scanning a Codebase section below.

🔐 Authentication Required

All endpoints except /health and /status require an API key.

Include this header in every request:

x-api-key: YOUR_API_KEY

Without this header, requests return 401 Unauthorized. Set your key as CHUNKER_API_KEY in your agent's environment.

Setup

Two environment variables are required before using this skill:

CHUNKER_API_URL   — Base URL of the hosted API.
                    Example: https://meaningful-chunker-production.up.railway.app
CHUNKER_API_KEY   — Your API key. Get a free key instantly at:
                    https://meaningful-chunker-production.up.railway.app/register/free
                    (100 queries/month, resets each calendar month, no credit card required)
                    Upgrade to Pro (2,000/month) at /upgrade.

Full OpenAPI spec available at: $CHUNKER_API_URL/docs

When to Use This Skill

Use one of the four query endpoints depending on intent:

All four endpoints accept the same request body and return the same response shape.

🚀 30-Second Quick Start

1. Scan a repo: POST /scan with {"repo_url": "https://github.com/owner/repo"}

2. Wait until: GET /status → "ready": true

3. Query: POST /query/architecture with {"query": "What does X do?"}

Scanning a Codebase

Scan a GitHub repo (recommended)

curl -s -X POST $CHUNKER_API_URL/scan \
  -H "Content-Type: application/json" \
  -H "x-api-key: $CHUNKER_API_KEY" \
  -d '{"repo_url": "https://github.com/owner/repo"}'

The system clones the repo, builds the graph, and deletes the local clone automatically. Supports any public GitHub, GitLab, or git URL — including /tree/main branch URLs. Repos larger than 300MB are rejected with a clear error.

Scan a local path (self-hosted instances only)

curl -s -X POST $CHUNKER_API_URL/scan \
  -H "Content-Type: application/json" \
  -H "x-api-key: $CHUNKER_API_KEY" \
  -d '{"project_path": "/path/to/project"}'

Check scan progress

curl $CHUNKER_API_URL/status

When "ready": true the graph is built and all query endpoints are available. Scanning typically takes 5–30 seconds depending on repo size.

How to Query

Request format

curl -s -X POST $CHUNKER_API_URL/query/architecture \
  -H "Content-Type: application/json" \
  -H "x-api-key: $CHUNKER_API_KEY" \
  -d '{"query": "What does the payment processor do?"}'

Replace /query/architecture with the appropriate endpoint for your intent.

Optional: session continuity

Pass a session_id to maintain context across related queries. The system remembers what you asked recently and biases results toward the same area of the codebase.

curl -s -X POST $CHUNKER_API_URL/query/debug \
  -H "Content-Type: application/json" \
  -H "x-api-key: $CHUNKER_API_KEY" \
  -d '{"query": "Why is the checkout flow failing?", "session_id": "my-investigation-001"}'

⚡ Quick Read Guide

For fastest results, read the response in this order:

1. answer_summary — one sentence telling you what the key component is and why it matters. Start here every time.
2. primary_path — the execution chain (A → B → C). Shows how things connect structurally.
3. CENTER tier chunks — the exact matches. These ARE the answer.
4. EPIPHANY tier chunks — critical cross-system connections. Don't skip these.
5. next_step — actionable follow-up already computed for you.

Everything else (BREAKTHROUGH, RELEVANT) is supporting context. Read it when you need depth, skip it when you don't.

Understanding the Response

Top-level synthesis fields

answer_summary      — One-sentence synthesis. Start here. Tells you what the
                      key component is, its role, and why it matters.

system_explanation  — What the relevant subsystem does as a whole. Multiple
                      components explained together.

primary_path        — The execution chain: "A → B → C". Shows how components
                      connect structurally. Most useful for debugging.

query_profile       — Which intent was detected (architecture/debug/refactor_risk/
                      philosophy). Confirms the system understood your query.

confidence          — "high" / "medium" / "low". Trust signal before reading context.

next_step           — Actionable follow-up. What to look at next.

Debug-specific fields (present on /query/debug responses)

root_cause_analysis.execution_timeline    — Call chain with [FAILING_CHUNK] bracketed
root_cause_analysis.root_cause_hypotheses — Ranked suspects with confidence + check instructions
root_cause_analysis.top_suspect           — Single highest-suspicion chunk with specific check

Refactor-specific fields (present on /query/refactor responses)

structural_authority.change_risk   — "untouchable" / "sensitive" / "local"
structural_authority.blast_radius  — How many chunks/files break if this changes
top_risks                          — Top 5 most dangerous chunks in the whole codebase

Context tiers

CENTER       (score=100)  — Exact match. This IS what you asked about.
EPIPHANY     (score≥99)   — Critical cross-component connection. Don't skip these.
BREAKTHROUGH (score≥82)   — Direct structural dependency. Important context.
RELEVANT     (score≥70)   — Meaningful but peripheral. Useful for broader understanding.

Each chunk includes:

• name — component identifier
• type — class / function / method / module_code / etc.
• file — source file path
• summary — one-line description
• why_matched — how the system found this chunk
• neighbors — adjacent components in the graph
• explanation (CENTER/EPIPHANY only) — roles, primary reason, importance summary

Example: Architecture Query

curl -s -X POST $CHUNKER_API_URL/query/architecture \
  -H "Content-Type: application/json" \
  -H "x-api-key: $CHUNKER_API_KEY" \
  -d '{"query": "What does the UserAuthenticator do?"}'

Read: answer_summary → primary_path → CENTER explanation.roles → BREAKTHROUGH neighbors

Example: Debug Query

curl -s -X POST $CHUNKER_API_URL/query/debug \
  -H "Content-Type: application/json" \
  -H "x-api-key: $CHUNKER_API_KEY" \
  -d '{"query": "Why is the login flow not working?"}'

Read: root_cause_analysis.top_suspect → execution_timeline → root_cause_hypotheses[0]

Example: Refactor Safety Query

curl -s -X POST $CHUNKER_API_URL/query/refactor \
  -H "Content-Type: application/json" \
  -H "x-api-key: $CHUNKER_API_KEY" \
  -d '{"query": "Can I safely change the database connection handler?"}'

Read: structural_authority.change_risk → blast_radius → advice → top_risks

Relevance Tiers — Scoring Reference

Results capped at: CENTER×3, EPIPHANY×5, BREAKTHROUGH×6, RELEVANT×5. Max 19 chunks per response.

Checking API Health

These endpoints are always open — no API key required:

curl $CHUNKER_API_URL/health

Returns {"status": "ok"} when the system is up and a project is indexed.

curl $CHUNKER_API_URL/status

Returns current graph stats: chunk count, edge count, cluster count, shortcut count, last scan time.

⚠️ Common Issues

• "no_scan_loaded" → You forgot to run /scan
• 401 Unauthorized → Missing x-api-key header

Notes

• Queries are natural language — no special syntax required. "Why is X broken?" works as well as "explain the architecture of X."
• The system is language-agnostic: Python, C++, JSON, Markdown, and plain text files are all indexed.
• Session memory persists within an API session (same session_id). Cross-session long-term focus is tracked via insight memory — frequently queried components surface higher automatically.
• Repo scans maintain memory during the active service lifecycle. The same repo URL will accumulate focus signals across queries until the service restarts.
• Large repos (10k+ files) may take up to 2 minutes to scan. Poll /status to check progress.