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Cekura is designed to work seamlessly with AI coding agents. Whether you’re using Claude Code, Cursor, or VS Code, you can manage your voice agent testing and observability directly from your development environment.

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Integration Options

MCP & Skills

Install Cekura Skills so your assistant knows Cekura workflows, plus the MCP server so it can act on your workspace. Recommended setup.

AGENTS.md preset

Portable fallback: drop an AGENTS.md file in your repo for assistants that don’t load Agent Skills.

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Quick Setup

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1. Install Skills, then connect MCP

First install Cekura Skills so your assistant knows Cekura workflows: 
npx skills add cekura-ai/cekura-skills --all
Claude Code users: install the plugin instead — it bundles Skills, slash commands, and MCP config in one step. See MCP & Skills setup.
Then connect the MCP server so your assistant can act on your workspace: 
claude mcp add --transport http cekura --scope user https://api.cekura.ai/mcp
OAuth opens a browser to sign in — no API key needed. For API-key auth, see API-key setup.

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2. Add AGENTS.md to Your Repo

Drop an AGENTS.md template into your project root. This teaches any AI assistant the Cekura-specific workflows and terminology for your project.

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3. Discover Documentation

Cekura’s documentation is available in agent-friendly formats:
ResourceURLPurpose
llms.txthttps://docs.cekura.ai/llms.txtPage index with descriptions for AI discovery
llms-full.txthttps://docs.cekura.ai/llms-full.txtFull documentation content in markdown
OpenAPI Spechttps://api.cekura.ai/api/schema/Complete API specification

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Common Agent Workflows

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Test Your Agent After a Deploy

User: "Run our regression tests against the staging agent"

AI: [Uses list-agents, then scenarios-run-scenarios-create]
"I've kicked off 12 scenarios against your staging agent.
Results will be ready in ~3 minutes. I'll check the results."

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Create Metrics from Agent Description

User: "Generate quality metrics for our customer support agent"

AI: [Uses metrics-generate-metrics-create]
"I've generated 5 metrics based on the agent's system prompt:
1. Greeting Quality (boolean)
2. Issue Resolution (boolean)
3. Empathy Score (numeric 1-5)
4. Policy Compliance (boolean)
5. Call Wrap-up Quality (boolean)"

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Review Call Quality

User: "Show me the worst performing calls from last week"

AI: [Uses call-logs-list with filters]
"Here are the 5 lowest-scoring calls from the past 7 days..."

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Generate Evaluators from Real Calls

User: "Create test scenarios from our recent support calls"

AI: [Uses call-logs-create-scenarios-create]
"I've created 8 test scenarios from your recent call logs.
Each scenario captures the conversation flow and expected outcomes."

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Best Practices

Begin with listing agents, viewing results, and reading call logs before using write operations like creating metrics or running scenarios.
If you have multiple projects, always specify the project_id parameter to scope your queries. This prevents cross-project data leakage.
Large responses (detailed transcripts, full result sets) can consume significant context. Use pagination parameters (page, limit) to control response size.
Use Cekura’s GitHub Actions integration alongside MCP to run evals on every PR and review results from your agent.

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Next Steps

Manual setup (Skills + MCP)

Install Skills, then configure the MCP server — for clients without a plugin

AGENTS.md Template

Copy-paste template for your project

API Reference

Complete API documentation

Testing Guide

Learn how to create and run evaluators