convex · MCP Server

Convex + Improvado MCP — Connect Your Data to AI

Improvado's MCP server connects Convex to Claude, Cursor, and other AI agents. Query your Convex data in natural language — no manual exports or API scripts required.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Instant Answers from Convex

Stop opening the Convex dashboard to inspect table data and function logs. Ask your AI agent to query your Convex database, inspect document structures, analyze function execution patterns, and surface data insights — all in plain language.

Example prompts

"Query the 'users' table in Convex and show me how many users signed up in the last 7 days, segmented by signup source."

20 min → 45 sec

"What are the most frequently called Convex functions in the last 24 hours, and do any have an error rate above 1%?"

25 min → 30 sec

"Show me the document structure for the 'orders' table. List all fields, their types, and any documents where 'status' is 'pending' for more than 48 hours."

15 min → 30 sec

Works with Claude ChatGPT Cursor +5

Write

Write: Automate Convex Actions

Run Convex mutations, trigger scheduled functions, update documents, and manage data — all through natural language. Handle one-off data operations and administrative tasks without writing queries in the dashboard.

Example prompts

"Run a Convex mutation to set the 'isActive' field to false for all user accounts that haven't logged in for 90 days."

30 min → 2 min

"Trigger the 'sendWeeklyDigest' scheduled function for all users in the premium tier immediately, without waiting for the scheduled time."

15 min → 30 sec

"Create a new document in the 'announcements' table with this content and set it to publish at 9am tomorrow."

10 min → 1 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Catch Convex Issues Before They Escalate

Set AI-powered watches on function error rates, data growth patterns, and scheduled job health. Get alerts before a silent Convex mutation failure cascades into a user-facing bug.

Example prompts

"Alert me if any Convex function has an error rate above 2% in a 15-minute rolling window."

Manual → auto

"Daily: send a Convex health summary — function call volume, error rate trends, database size growth, and any scheduled functions that failed to execute."

1 hr → auto

"Notify me if the 'payments' table has no new documents written for more than 2 hours during business hours — possible upstream failure."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Debugging Production Data Requires Console Access Not Everyone Has

The problem

When something goes wrong in a Convex-backed application, developers need to inspect production data to understand the state. This requires direct console access to the Convex dashboard — which not everyone on the team has, and which requires knowing the exact table structure and query syntax to navigate. Non-technical stakeholders asking 'how many users are affected?' have no self-service option.

How MCP solves it

Your AI agent gives everyone natural language access to Convex production data — without console credentials. Ask any question about the data, and the agent translates it into the appropriate Convex query, executes it safely, and returns a plain-language answer with the relevant documents.

Try asking

How many user accounts are currently in an error state in the Convex database? List their IDs, the error code, and when the error first appeared.

Answer in seconds

All data sources, one query

Challenge 2

Scheduled Functions Fail Silently Until Users Notice

The problem

Convex scheduled functions — nightly cleanups, weekly digests, data sync jobs — can fail silently if there's no monitoring in place. The first sign of failure is often a user complaint: 'I didn't get my weekly summary email' or 'the old data is still showing.' By the time it's caught, multiple scheduled runs may have been missed, requiring a manual backfill.

How MCP solves it

Improvado MCP monitors your Convex scheduled function execution automatically. Your AI agent checks each scheduled job's last execution time and status, flags missed runs, and can trigger a manual execution to catch up — turning reactive incident response into proactive monitoring.

Try asking

Check all Convex scheduled functions. Which ones haven't run successfully in the last 48 hours? For any that failed, show me the error and trigger a retry.

Full detail preserved

No data loss on export

Challenge 3

Schema Evolution Creates Data Inconsistencies Across Document Versions

The problem

Convex's document-based model allows flexible schemas, but this flexibility creates a common problem: old documents don't automatically get new fields added in newer versions. Over time, the same collection accumulates documents with different field sets — some with the new 'tier' field, some without. Queries that assume all documents have the same shape return inconsistent results, and debugging requires manually sampling documents across time ranges.

How MCP solves it

Ask your AI agent to audit Convex table schema consistency. It samples documents across creation date ranges, identifies missing or mismatched fields, and returns a report of which documents need migration — before inconsistency causes a production issue.

Try asking

Audit the 'subscriptions' table in Convex. Identify any documents that are missing fields present in the most recent 100 documents. How many legacy documents need migration, and what fields are they missing?

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What is Convex MCP?

Convex MCP is a Model Context Protocol server that connects your Convex backend to AI agents like Claude, ChatGPT, and Gemini. It lets you query database tables, run functions, inspect document structures, and monitor scheduled jobs — all in natural language, without opening the Convex dashboard — all through Improvado's hosted MCP server.

Which Convex data can I access through the MCP server?

All tables and documents in your Convex database (subject to your API key permissions), function definitions and execution history, scheduled function status, error logs, and usage metrics. The AI agent can query any table and run read queries or mutations.

Can the AI agent run mutations and modify data, or only read documents?

Both. Read operations include querying tables, sampling documents, and inspecting function behavior. Write operations include running mutations to update documents, triggering scheduled functions manually, and creating new documents. All write operations require an API key with mutation permissions.

Is this safe to use against a production Convex environment?

Yes, with appropriate safeguards. Improvado recommends using a read-only API key for query-only use cases. For mutation access, all write operations are explicitly confirmed before execution. The MCP server does not execute mutations automatically without a clear write instruction in your prompt.

Is my Convex data secure through the MCP server?

Yes. Improvado stores Convex API keys in an encrypted vault certified to SOC 2 Type II. The AI agent never accesses credentials directly. All requests are proxied through Improvado's secure layer.

How quickly can I set this up?

Under 60 seconds. Generate a Convex API key from your deployment settings, add the MCP server URL to your AI agent config, and you're ready to query. No additional configuration is required for read-only access — all through Improvado's hosted MCP server.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms