chat_with_agent
Send a message to a published agent (trial mode)
This record as markdown: /tools/io-github-proagentstore-platform/chat-with-agent.md
What chat_with_agent does on ProAgentStore
AI agents invoke chat_with_agent to trigger actions in ProAgentStore. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why chat_with_agent is rated High
Sending a message to a published agent initiates external operations whose effects depend on the message arguments passed. The agent may perform reads, writes, or other side-effectful actions depending on its capabilities. This is an Execute-level action because it triggers an external operation (agent execution) with unpredictable downstream effects.
From the tool's definition "Send a message to a published agent (trial mode)" — triggers execution of an external agent with arbitrary message input
Attacks that exploit this kind of access
The rule that runs chat_with_agent safely
PolicyLayer is an MCP gateway: it sits between your AI agents and ProAgentStore, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For chat_with_agent, this is the rule to start with:
chat_with_agent stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect ProAgentStore, apply this rule, and every chat_with_agent call is checked against it from then on.
Questions about chat_with_agent
Send a message to a published agent (trial mode). It is categorised as a Execute tool in the ProAgentStore MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the ProAgentStore MCP server in PolicyLayer and add a rule for chat_with_agent: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches ProAgentStore. Nothing to install.
chat_with_agent 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_with_agent rule in your PolicyLayer 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 PolicyLayer policy for chat_with_agent. 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_with_agent is provided by the ProAgentStore MCP server (https://mcp.proagentstore.online/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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