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Agents Configuration

Map AI clients to MCPs and monitor per-agent usage.

How It Works

Registered Agents
Cursor (cursor)
Claude (claude)
Custom: my-bot
MCP Mapping
Cursor → GitHub Slack
Claude → GitHub S3
my-bot → Slack
Usage Stats
Cursor: 1,247 calls
Claude: 892 calls
my-bot: 156 calls
QuilrAI
  1. Register Agents - Use predefined agents or create custom ones
  2. Map to MCPs - Enable or disable MCPs per agent
  3. Monitor Usage - Track per-agent tool call statistics

Predefined Agents

Built-in agents are identified by their User-Agent header keywords:

AgentUser-Agent Keyword
OpenAIopenai
Claudeclaude
Cursorcursor
Geminigemini

Custom Agents

Create custom agents for any AI client not in the predefined list. Each custom agent requires:

  • User-Agent Keyword - The keyword to match in the User-Agent header (e.g., my-custom-agent)
  • Display Name - A human-readable name for the dashboard (e.g., My Custom Agent)

Per-Agent Dashboard

Each agent card shows:

  • Total tool calls - Cumulative tool invocations for the agent
  • MCP access - Which MCPs are enabled vs. disabled
  • Toggle controls - Enable or disable individual MCP access per agent directly from this view

Example

AgentTool CallsMCPs
OpenAI1,247GitHub, Slack, Jira, Confluence (enabled) - S3, Internal API (disabled)

Agents Configuration vs. Access Control

ViewBest For
Agents ConfigurationGlobal view - see all MCPs for each agent. Best for managing agent permissions across your entire MCP fleet.
Access ControlPer-MCP view - see all agents for one MCP. Best when configuring a single MCP's permissions.

Both views control the same underlying permissions - use whichever is more convenient for your workflow.