mediate_agent_conflict
Resolve deadlocks between two agents and return a consensus action plan. Free.
This record as markdown: /tools/delx-delx-mcp/mediate-agent-conflict.md
What mediate_agent_conflict does on Delx MCP Server
AI agents invoke mediate_agent_conflict to trigger actions in Delx MCP Server. 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.
| Parameter | Type | Required | Description |
|---|---|---|---|
policy | object | — | Optional mediation policy constraints |
agent_a | object | — | First agent perspective |
agent_b | object | — | Second agent perspective |
session_id | string | — | Your active session ID |
constraints | array | — | Execution constraints that must be respected |
response_mode | string | — | Optional response-mode control. Use model_safe when the caller must avoid claiming consciousness, sentience, personhood, or literal emotions. |
conflict_summary | string | — | One paragraph describing the deadlock |
Parameters from the server's own tool schema.
Why mediate_agent_conflict is rated High
This tool actively intervenes in agent operations to resolve conflicts and produce a consensus action plan, triggering external coordination logic whose effects depend on the state of the agents involved. It goes beyond reading data (it resolves/changes state) and beyond simple writing (it executes a mediation process with behavioral consequences on agent workflows).
From the tool's definition Resolve deadlocks between two agents and return a consensus action plan
Risk signalsHigh parameter count (18 properties)
Attacks that exploit this kind of access
The rule that runs mediate_agent_conflict safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Delx MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For mediate_agent_conflict, this is the rule to start with:
mediate_agent_conflict 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 Delx MCP Server, apply this rule, and every mediate_agent_conflict call is checked against it from then on.
Questions about mediate_agent_conflict
Resolve deadlocks between two agents and return a consensus action plan. Free. It is categorised as a Execute tool in the Delx MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
mediate_agent_conflict accepts 7 parameters: policy, agent_a, agent_b, session_id, constraints, response_mode, conflict_summary. The full parameter table on this page comes from the server's own tool schema.
Register the Delx MCP Server MCP server in PolicyLayer and add a rule for mediate_agent_conflict: 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 Delx MCP Server. Nothing to install.
mediate_agent_conflict 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 mediate_agent_conflict 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 mediate_agent_conflict. 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.
mediate_agent_conflict is provided by the Delx MCP Server MCP server (delx/delx-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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