Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/onenote-cost-tuning/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill onenote-cost-tuning. Copyright stays with the author (MIT).

OneNote Cost Tuning

Overview

OneNote Graph API calls have no per-request cost — they are included in every Microsoft 365 E3/E5/Business license. However, rate limits create an effective ceiling that functions like a cost constraint: 600 requests per user per 60 seconds and 10,000 requests per app per 10 minutes at the tenant level. Exceeding these limits returns 429 errors with Retry-After headers, degrading user experience the same way budget overruns degrade service. This skill covers the practical optimization strategies that keep you well under those ceilings: metadata caching, JSON batch requests, delta sync, payload minimization with $select/$expand, and content deduplication. A naive integration that polls every user's notebooks every minute burns through the tenant limit in under 10 minutes. An optimized one handles thousands of users within the same budget.

Prerequisites

• Microsoft 365 license (E3/E5/Business) — OneNote API is included, no additional billing
• Azure AD app registration with delegated permissions
• Python: pip install msgraph-sdk azure-identity or Node: npm install @microsoft/microsoft-graph-client @azure/identity
• Understanding of HTTP caching headers (ETag, If-None-Match)

Instructions

Licensing Model and True Cost

The real cost is operational: every 429 response adds latency, retry logic consumes compute, and throttled users see failures. Optimization is about reliability, not billing.

Strategy 1: Cache Metadata Aggressively

Notebook and section metadata changes rarely (names, IDs, hierarchy). Cache it locally and refresh on a schedule, not per-request:

interface CachedMetadata {
  notebooks: any[];
  sections: Map<string, any[]>;  // notebookId -> sections
  fetchedAt: number;
  ttlMs: number;
}

class MetadataCache {
  private cache: CachedMetadata = {
    notebooks: [],
    sections: new Map(),
    fetchedAt: 0,
    ttlMs: 15 * 60 * 1000,  // 15 minutes — notebooks/sections rarely change
  };

  isStale(): boolean {
    return Date.now() - this.cache.fetchedAt > this.cache.ttlMs;
  }

  async getNotebooks(client: any): Promise<any[]> {
    if (!this.isStale() && this.cache.notebooks.length > 0) {
      return this.cache.notebooks;  // 0 API calls
    }
    const response = await client.api("/me/onenote/notebooks")
      .select("id,displayName,lastModifiedDateTime")
      .get();
    this.cache.notebooks = response.value;
    this.cache.fetchedAt = Date.now();
    return this.cache.notebooks;  // 1 API call
  }

  async getSections(client: any, notebookId: string): Promise<any[]> {
    if (!this.isStale() && this.cache.sections.has(notebookId)) {
      return this.cache.sections.get(notebookId)!;
    }
    const response = await client
      .api(`/me/onenote/notebooks/${notebookId}/sections`)
      .select("id,displayName")
      .get();
    this.cache.sections.set(notebookId, response.value);
    return response.value;
  }

  invalidate(): void {
    this.cache.fetchedAt = 0;
  }
}

Savings: A typical app listing notebooks 100 times/hour drops from 100 calls to 4 calls (one refresh per 15-minute TTL window).

Strategy 2: JSON Batch Requests

The $batch endpoint combines up to 20 requests into a single HTTP call, reducing call count by up to 20x:

import httpx

async def batch_get_sections(access_token: str, notebook_ids: list[str]) -> dict:
    """Fetch sections for multiple notebooks in a single HTTP call."""
    requests = []
    for i, nb_id in enumerate(notebook_ids[:20]):  # Max 20 per batch
        requests.append({
            "id": str(i),
            "method": "GET",
            "url": f"/me/onenote/notebooks/{nb_id}/sections?$select=id,displayName",
        })

    async with httpx.AsyncClient() as http:
        response = await http.post(
            "https://graph.microsoft.com/v1.0/$batch",
            headers={
                "Authorization": f"Bearer {access_token}",
                "Content-Type": "application/json",
            },
            json={"requests": requests},
        )
        batch_result = response.json()

    # Map responses back to notebook IDs
    result = {}
    for resp in batch_result["responses"]:
        idx = int(resp["id"])
        nb_id = notebook_ids[idx]
        if resp["status"] == 200:
            result[nb_id] = resp["body"]["value"]
        else:
            result[nb_id] = []  # Handle individual failures gracefully
    return result

Savings: Fetching sections for 20 notebooks: 20 calls becomes 1 call. For 100 notebooks, 100 calls becomes 5 calls.

Strategy 3: Delta Sync Instead of Full Sync

Delta queries return only changes since your last sync, replacing full-list operations that grow linearly with data size:

class DeltaSyncer {
  private deltaLinks: Map<string, string> = new Map();

  async syncPages(client: any, sectionId: string): Promise<{
    added: any[];
    modified: any[];
    deleted: string[];
  }> {
    const deltaLink = this.deltaLinks.get(sectionId);
    const url = deltaLink || `/me/onenote/sections/${sectionId}/pages/delta`;

    const response = await client.api(url).get();
    const result = { added: [] as any[], modified: [] as any[], deleted: [] as string[] };

    for (const page of response.value || []) {
      if (page["@removed"]) {
        result.deleted.push(page.id);
      } else if (deltaLink) {
        result.modified.push(page);
      } else {
        result.added.push(page);
      }
    }

    // Store the delta link for next sync
    if (response["@odata.deltaLink"]) {
      this.deltaLinks.set(sectionId, response["@odata.deltaLink"]);
    }

    return result;
  }
}

Savings: A section with 500 pages that changes 3 pages/hour: full sync = 500+ items per response, delta sync = 3 items. Call count may be same (1), but payload size drops 99%.

Strategy 4: Payload Minimization with $select and $expand

Every field you do not request is bandwidth you save and parsing you skip:

# BAD: Returns all fields including content URLs, permissions, links (2-5 KB per page)
GET /me/onenote/sections/{id}/pages

# GOOD: Returns only what you need (200-300 bytes per page)
GET /me/onenote/sections/{id}/pages?$select=id,title,createdDateTime,lastModifiedDateTime&$top=50&$orderby=lastModifiedDateTime desc

Use $expand to eliminate follow-up calls:

# Without $expand: 1 call for notebooks + N calls for sections = N+1 calls
GET /me/onenote/notebooks
GET /me/onenote/notebooks/{id1}/sections
GET /me/onenote/notebooks/{id2}/sections

# With $expand: 1 call total
GET /me/onenote/notebooks?$expand=sections($select=id,displayName)&$select=id,displayName

Strategy 5: Content Deduplication

Hash page content before writing to avoid duplicate POST calls:

import hashlib

def content_hash(html_body: str) -> str:
    """Generate a stable hash of page content for dedup."""
    return hashlib.sha256(html_body.encode("utf-8")).hexdigest()

class DeduplicatedWriter:
    def __init__(self):
        self.written_hashes: dict[str, str] = {}  # hash -> page_id

    async def write_page(self, client, section_id: str, title: str, html_body: str):
        h = content_hash(html_body)
        if h in self.written_hashes:
            return self.written_hashes[h]  # Skip duplicate write

        full_html = (
            f"<html><head><title>{title}</title></head>"
            f"<body>{html_body}</body></html>"
        )
        page = await client.me.onenote.sections.by_onenote_section_id(
            section_id
        ).pages.post(content=full_html.encode())
        self.written_hashes[h] = page.id
        return page.id

Cost Modeling Template

Estimate your API call budget per user per day:

At 100 users, you are using 47% of the 10-minute tenant budget — safe headroom. At 250+ users, consider Graph metered billing or reduce polling frequency.

Monitoring Dashboard Metrics

Track these metrics to detect optimization drift:

import time
from collections import defaultdict

class ApiMetrics:
    def __init__(self):
        self.calls_per_user: dict[str, int] = defaultdict(int)
        self.throttle_count = 0
        self.total_latency_ms = 0.0
        self.call_count = 0

    def record_call(self, user_id: str, latency_ms: float, status: int):
        self.calls_per_user[user_id] += 1
        self.total_latency_ms += latency_ms
        self.call_count += 1
        if status == 429:
            self.throttle_count += 1

    def report(self) -> dict:
        return {
            "total_calls": self.call_count,
            "avg_latency_ms": self.total_latency_ms / max(self.call_count, 1),
            "throttle_rate": self.throttle_count / max(self.call_count, 1),
            "top_users": sorted(
                self.calls_per_user.items(), key=lambda x: x[1], reverse=True
            )[:10],
        }

Alert thresholds:

• Throttle rate > 1%: investigate hotspot user or batch consolidation
• Avg latency > 2000ms: Graph service degradation or oversized payloads
• Single user > 300 calls/hour: likely missing cache or polling too frequently

Output

After applying this skill, your OneNote integration will have: metadata caching reducing repetitive calls by 95%, batch requests combining up to 20 calls into 1, delta sync for incremental page updates, minimized payloads via $select/$expand, content deduplication preventing duplicate writes, and a monitoring framework to detect optimization regression.

Error Handling

Examples

Quick audit of current API usage:

# Check rate limit headers in any Graph response
curl -sI -H "Authorization: Bearer $TOKEN" \
  "https://graph.microsoft.com/v1.0/me/onenote/notebooks" | \
  grep -i "ratelimit\|retry-after\|x-ms"

Batch request via curl:

curl -X POST "https://graph.microsoft.com/v1.0/\$batch" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "requests": [
      {"id": "1", "method": "GET", "url": "/me/onenote/notebooks?$select=id,displayName"},
      {"id": "2", "method": "GET", "url": "/me/onenote/sections?$select=id,displayName&$top=50"}
    ]
  }'

Resources

• OneNote API Overview
• Best Practices
• Get Content
• Graph Explorer
• Error Codes
• Graph API Reference

Next Steps

• Apply onenote-rate-limits for detailed Retry-After handling and queue-based throttling
• Use onenote-performance-tuning for response time optimization
• See onenote-prod-checklist for full production readiness review