Send a message to the AgentLed AI agent and get a response. The agent can reason, plan, and build workflows through natural language conversation — no need to construct pipeline JSON manually. Use this tool when you want to: - Build a workflow from a high-level description ("Create a lead enrich...
Part of the Agentled MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents invoke chat to trigger processes or run actions in Agentled. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
chat can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. Intercept enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
tools:
chat:
rules:
- action: allow
rate_limit:
max: 10
window: 60
validate:
required_args: trueSee the full Agentled policy for all 56 tools.
Agents calling execute-class tools like chat have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Execute risk category across the catalogue. The same policy patterns (rate-limit, validate) apply to each.
chat is one of the high-risk operations in Agentled. For the full severity-focused view — only the high-risk tools with their recommended policies — see the breakdown for this server, or browse all high-risk tools across every MCP server.
Send a message to the AgentLed AI agent and get a response. The agent can reason, plan, and build workflows through natural language conversation — no need to construct pipeline JSON manually. Use this tool when you want to: - Build a workflow from a high-level description ("Create a lead enrichment workflow for SaaS companies") - Get recommendations on how to structure a workflow - Ask questions about available integrations or capabilities - Iterate on workflow design through conversation The agent has access to the same planning tools, workflow builder, and workspace context as the in-app chat. For multi-turn conversations, pass the session_id returned from the first message to maintain context across messages. Example: chat("Build me a workflow that takes a LinkedIn company URL, enriches the data, and scores it by ICP fit"). It is categorised as a Execute tool in the Agentled MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Add a rule in your Intercept YAML policy under the tools section for chat. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Agentled MCP server.
chat is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the chat rule in your Intercept policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the Intercept policy for chat. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
chat is provided by the Agentled MCP server (@agentled/mcp-server). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Open source. One binary. Zero dependencies.
npx -y @policylayer/intercept