Granola local dev loop
'Access Granola meeting data programmatically for developer workflows.From its SKILL.md
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SKILL.md
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Granola Local Dev Loop
Overview
Access Granola meeting data programmatically using three methods: the local cache file (zero-auth, offline), the MCP server (AI agent integration), or the Enterprise API (workspace-wide access). Build developer workflows that turn meeting outcomes into code tasks, documentation, and project artifacts.
Prerequisites
- Granola installed with meetings captured
- Node.js 18+ or Python 3.10+ for scripts
- For MCP: Claude Code, Cursor, or another MCP-compatible client
- For Enterprise API: Business/Enterprise plan + API key
Instructions
Step 1 — Read the Local Cache (Zero Auth)
Granola stores meeting data in a local JSON cache file:
# macOS cache location
CACHE_FILE="$HOME/Library/Application Support/Granola/cache-v3.json"
# Check if cache exists and get size
ls -lh "$CACHE_FILE"
The cache has a double-JSON structure (JSON string inside JSON):
#!/usr/bin/env python3
"""Extract meetings from Granola local cache."""
import json
from pathlib import Path
CACHE_PATH = Path.home() / "Library/Application Support/Granola/cache-v3.json"
def load_granola_cache():
raw = json.loads(CACHE_PATH.read_text())
# Cache contains a JSON string that needs secondary parsing
state = json.loads(raw) if isinstance(raw, str) else raw
data = state.get("state", state)
return {
"documents": data.get("documents", {}),
"transcripts": data.get("transcripts", {}),
"meetings_metadata": data.get("meetingsMetadata", {}),
}
cache = load_granola_cache()
docs = cache["documents"]
print(f"Found {len(docs)} meetings in local cache")
# List recent meetings
for doc_id, doc in sorted(docs.items(),
key=lambda x: x[1].get("updated_at", ""),
reverse=True)[:10]:
print(f" {doc.get('title', 'Untitled')} — {doc.get('updated_at', 'N/A')}")
Step 2 — Set Up Granola MCP Server
Granola's official MCP integration connects meeting context to AI tools:
// claude_desktop_config.json or .mcp.json
{
"mcpServers": {
"granola": {
"command": "npx",
"args": ["-y", "granola-mcp-server"]
}
}
}
With MCP connected, Claude Code and Cursor can:
- Search across all your meetings by topic or person
- Pull context from specific meetings into coding sessions
- Create tickets based on discussed bugs or features
- Scaffold code based on architectural decisions from meetings
Community MCP servers with additional features:
pedramamini/GranolaMCP— CLI + programmatic + MCP access, reads local cachemishkinf/granola-mcp— semantic search with LanceDB vector embeddingsproofgeist/granola-mcp-server— lightweight local cache reader
Step 3 — Extract Action Items to Dev Tools
#!/usr/bin/env python3
"""Extract action items from Granola notes and create GitHub issues."""
import json, re, subprocess
from pathlib import Path
def extract_action_items(note_content: str) -> list[dict]:
"""Parse action items from enhanced Granola notes."""
items = []
# Matches: - [ ] @person: task description
pattern = r'- \[ \] @?(\w+):?\s+(.+)'
for match in re.finditer(pattern, note_content):
items.append({
"assignee": match.group(1),
"task": match.group(2).strip(),
})
return items
def create_github_issue(repo: str, title: str, body: str, assignee: str):
"""Create a GitHub issue using gh CLI."""
cmd = [
"gh", "issue", "create",
"--repo", repo,
"--title", title,
"--body", body,
"--assignee", assignee,
]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode == 0:
print(f" Created: {result.stdout.strip()}")
else:
print(f" Error: {result.stderr.strip()}")
# Usage with cache data
cache = load_granola_cache() # from Step 1
for doc_id, doc in cache["documents"].items():
content = doc.get("last_viewed_panel", {})
# ProseMirror content needs text extraction
text = json.dumps(content) # simplified — parse nodes for production
actions = extract_action_items(text)
for action in actions:
print(f"[{action['assignee']}] {action['task']}")
Step 4 — Sync Meeting Outcomes to Project Docs
#!/bin/bash
set -euo pipefail
# Sync latest Granola meeting notes to project documentation
NOTES_DIR="$HOME/dev/meeting-notes"
mkdir -p "$NOTES_DIR"
# Extract recent meeting titles and dates using Python
python3 -c "
import json
from pathlib import Path
cache_path = Path.home() / 'Library/Application Support/Granola/cache-v3.json'
if cache_path.exists():
raw = json.loads(cache_path.read_text())
state = json.loads(raw) if isinstance(raw, str) else raw
data = state.get('state', state)
docs = data.get('documents', {})
for doc_id, doc in sorted(docs.items(),
key=lambda x: x[1].get('updated_at', ''),
reverse=True)[:5]:
title = doc.get('title', 'Untitled').replace(' ', '-').lower()
date = doc.get('created_at', 'unknown')[:10]
print(f'{date}_{title}')
"
Step 5 — Git Integration Pattern
Reference Granola meetings in commits and PRs:
# Reference meeting in commit message
git commit -m "feat: implement user onboarding flow
Per meeting 2026-03-22 'Sprint Planning Q1':
- Agreed on 3-step wizard approach
- Sarah approved the design mockups
- Due by April 15
Action items from Granola note: [link]"
Output
- Local cache accessible for offline meeting data reads
- MCP server connected for AI-assisted meeting context
- Action item extraction pipeline ready
- Meeting-to-dev-tools sync established
Error Handling
| Error | Cause | Fix |
|---|---|---|
| Cache file not found | Granola not installed or never launched | Install Granola and capture at least one meeting |
| JSON parse error | Double-JSON structure not handled | Parse the outer string first, then parse the inner object |
| MCP server not connecting | Wrong config path | Verify claude_desktop_config.json location for your OS |
| Empty transcripts | Transcript stored separately from document | Check cache["transcripts"] keyed by document ID |
| Stale cache data | Cache not refreshed | Restart Granola to force cache update |
Resources
- Granola MCP Announcement
- GranolaMCP (cache-based)
- Reverse-Engineered API Docs
- Granola Enterprise API
Next Steps
Proceed to granola-sdk-patterns for Zapier automation workflows.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.