documenso-performance-tuning
Optimize Documenso integration performance with caching, batching, and efficient patterns. Use when improving response times, reducing API calls, or optimizing bulk document operations. Trigger with phrases like "documenso performance", "optimize documenso", "documenso caching", "documenso batch operations".
Install
mkdir -p .claude/skills/documenso-performance-tuning && curl -L -o skill.zip "https://mcp.directory/api/skills/download/8910" && unzip -o skill.zip -d .claude/skills/documenso-performance-tuning && rm skill.zipInstalls to .claude/skills/documenso-performance-tuning
About this skill
Documenso Performance Tuning
Overview
Optimize Documenso integrations for speed and efficiency. Key strategies: reduce API round-trips with templates, cache document metadata, batch operations with concurrency control, and use async processing for bulk signing workflows.
Prerequisites
- Working Documenso integration
- Redis or in-memory cache (recommended)
- Completed
documenso-sdk-patternssetup
Instructions
Step 1: Reduce API Calls with Templates
The biggest performance win: templates reduce a multi-step document creation (create + upload + add recipients + add fields + send = 5+ calls) to just 2 calls (create from template + send).
// WITHOUT templates: 5+ API calls per document
async function createDocumentManual(signer: { email: string; name: string }) {
const doc = await client.documents.createV0({ title: "Contract" }); // 1
await client.documents.setFileV0(doc.documentId, { file: pdfBlob }); // 2
const recip = await client.documentsRecipients.createV0(doc.documentId, { // 3
email: signer.email, name: signer.name, role: "SIGNER",
});
await client.documentsFields.createV0(doc.documentId, { // 4
recipientId: recip.recipientId, type: "SIGNATURE",
pageNumber: 1, pageX: 10, pageY: 80, pageWidth: 30, pageHeight: 5,
});
await client.documents.sendV0(doc.documentId); // 5
}
// WITH templates: 2 API calls per document
async function createDocumentFromTemplate(templateId: number, signer: { email: string; name: string }) {
const res = await fetch( // 1
`${BASE}/templates/${templateId}/create-document`,
{
method: "POST",
headers: { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" },
body: JSON.stringify({
title: `Contract — ${signer.name}`,
recipients: [{ email: signer.email, name: signer.name, role: "SIGNER" }],
}),
}
);
const doc = await res.json();
await fetch(`${BASE}/documents/${doc.documentId}/send`, { // 2
method: "POST",
headers: { Authorization: `Bearer ${API_KEY}` },
});
}
Step 2: Cache Document Metadata
// src/cache/documenso-cache.ts
import NodeCache from "node-cache";
const cache = new NodeCache({ stdTTL: 300, checkperiod: 60 }); // 5 min TTL
export async function getCachedDocument(client: Documenso, documentId: number) {
const key = `doc:${documentId}`;
const cached = cache.get(key);
if (cached) return cached;
const doc = await client.documents.getV0(documentId);
// Only cache completed documents (immutable)
if (doc.status === "COMPLETED") {
cache.set(key, doc, 3600); // 1 hour for completed
} else {
cache.set(key, doc, 30); // 30 seconds for in-progress
}
return doc;
}
// Invalidate on webhook events
export function invalidateDocument(documentId: number) {
cache.del(`doc:${documentId}`);
}
Step 3: Batch Operations with Concurrency Control
// src/batch/documenso-batch.ts
import PQueue from "p-queue";
const queue = new PQueue({
concurrency: 5, // Max 5 concurrent API calls
interval: 1000, // Per second window
intervalCap: 10, // Max 10 per second
});
export async function batchCreateDocuments(
client: Documenso,
templateId: number,
signers: Array<{ email: string; name: string; company: string }>
): Promise<Array<{ email: string; documentId?: number; error?: string }>> {
const results = await Promise.allSettled(
signers.map((signer) =>
queue.add(async () => {
const res = await fetch(
`https://app.documenso.com/api/v1/templates/${templateId}/create-document`,
{
method: "POST",
headers: {
Authorization: `Bearer ${process.env.DOCUMENSO_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
title: `Agreement — ${signer.company}`,
recipients: [{ email: signer.email, name: signer.name, role: "SIGNER" }],
}),
}
);
if (!res.ok) throw new Error(`HTTP ${res.status}`);
const doc = await res.json();
// Send immediately
await fetch(
`https://app.documenso.com/api/v1/documents/${doc.documentId}/send`,
{
method: "POST",
headers: { Authorization: `Bearer ${process.env.DOCUMENSO_API_KEY}` },
}
);
return { email: signer.email, documentId: doc.documentId };
})
)
);
return results.map((r, i) => {
if (r.status === "fulfilled") return r.value as any;
return { email: signers[i].email, error: (r.reason as Error).message };
});
}
Step 4: Async Processing with Background Jobs
// src/jobs/signing-queue.ts
import Bull from "bull";
const signingQueue = new Bull("documenso-signing", process.env.REDIS_URL!);
// Producer: queue signing requests
export async function queueSigningRequest(data: {
templateId: number;
signerEmail: string;
signerName: string;
}) {
const job = await signingQueue.add(data, {
attempts: 3,
backoff: { type: "exponential", delay: 5000 },
});
return job.id;
}
// Consumer: process in background
signingQueue.process(5, async (job) => {
const { templateId, signerEmail, signerName } = job.data;
// Create and send document...
return { status: "sent" };
});
signingQueue.on("completed", (job, result) => {
console.log(`Job ${job.id} completed: ${JSON.stringify(result)}`);
});
signingQueue.on("failed", (job, err) => {
console.error(`Job ${job.id} failed: ${err.message}`);
});
Step 5: Efficient Pagination
// Paginate through all documents without loading everything into memory
async function* iterateDocuments(client: Documenso, perPage = 50) {
let page = 1;
while (true) {
const { documents } = await client.documents.findV0({
page,
perPage,
orderByColumn: "createdAt",
orderByDirection: "desc",
});
for (const doc of documents) {
yield doc;
}
if (documents.length < perPage) break; // Last page
page++;
}
}
// Usage: process all documents without memory issues
for await (const doc of iterateDocuments(client)) {
if (doc.status === "COMPLETED") {
await archiveDocument(doc.id);
}
}
Performance Targets
| Operation | Target | If Exceeded |
|---|---|---|
| Single document create | < 500ms | Check network latency |
| Template create + send | < 1s | Normal for template workflow |
| Batch of 100 documents | < 30s | Use concurrency 5-10 |
| Document list (page) | < 300ms | Add caching layer |
| Webhook processing | < 100ms | Process async, respond 200 immediately |
Error Handling
| Performance Issue | Cause | Solution |
|---|---|---|
| Slow responses | No connection reuse | Use singleton client pattern |
| Rate limit errors | Too many concurrent calls | Use p-queue with concurrency cap |
| Memory issues | Loading all documents | Use async generator pagination |
| Queue backlog | Slow processing | Increase worker concurrency |
Resources
Next Steps
For cost optimization, see documenso-cost-tuning.
More by jeremylongshore
View all skills by jeremylongshore →You might also like
flutter-development
aj-geddes
Build beautiful cross-platform mobile apps with Flutter and Dart. Covers widgets, state management with Provider/BLoC, navigation, API integration, and material design.
drawio-diagrams-enhanced
jgtolentino
Create professional draw.io (diagrams.net) diagrams in XML format (.drawio files) with integrated PMP/PMBOK methodologies, extensive visual asset libraries, and industry-standard professional templates. Use this skill when users ask to create flowcharts, swimlane diagrams, cross-functional flowcharts, org charts, network diagrams, UML diagrams, BPMN, project management diagrams (WBS, Gantt, PERT, RACI), risk matrices, stakeholder maps, or any other visual diagram in draw.io format. This skill includes access to custom shape libraries for icons, clipart, and professional symbols.
ui-ux-pro-max
nextlevelbuilder
"UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 8 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient."
godot
bfollington
This skill should be used when working on Godot Engine projects. It provides specialized knowledge of Godot's file formats (.gd, .tscn, .tres), architecture patterns (component-based, signal-driven, resource-based), common pitfalls, validation tools, code templates, and CLI workflows. The `godot` command is available for running the game, validating scripts, importing resources, and exporting builds. Use this skill for tasks involving Godot game development, debugging scene/resource files, implementing game systems, or creating new Godot components.
nano-banana-pro
garg-aayush
Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image) API. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports both text-to-image generation and image-to-image editing with configurable resolution (1K default, 2K, or 4K for high resolution). DO NOT read the image file first - use this skill directly with the --input-image parameter.
pdf-to-markdown
aliceisjustplaying
Convert entire PDF documents to clean, structured Markdown for full context loading. Use this skill when the user wants to extract ALL text from a PDF into context (not grep/search), when discussing or analyzing PDF content in full, when the user mentions "load the whole PDF", "bring the PDF into context", "read the entire PDF", or when partial extraction/grepping would miss important context. This is the preferred method for PDF text extraction over page-by-page or grep approaches.
Related MCP Servers
Browse all serversOptimize Facebook ad campaigns with AI-driven insights, creative analysis, and campaign control in Meta Ads Manager for
Fast, local-first web content extraction for LLMs. Scrape, crawl, extract structured data — all from Rust. CLI, REST API
Supercharge AI tools with Kagi MCP: fast google web search API, powerful ai summarizer, and seamless ai summary tool int
Experience high-performance CCXT MCP server for seamless cryptocurrency exchange integration.
Use Google Lighthouse to check web page performance and optimize website speed. Try our landing page optimizer for bette
Redshift Utils offers essential Amazon Redshift database admin tools for health monitoring, query analysis, and automate
Stay ahead of the MCP ecosystem
Get weekly updates on new skills and servers.