Medium Risk

generate_retry_message

Given a tool name, validation error, and attempted args, build the canonical LLM-facing retry feedback message. Uses agentvet's ToolArgError.toLLMFeedback() formatting so the wording matches what runtime callers see.

Part of the Agentvet MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.

AI agents use generate_retry_message to create or modify resources in Agentvet. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call generate_retry_message repeatedly, creating or modifying resources faster than any human could review. Intercept's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Agentvet.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

io-github-mukundakatta-agentvet.yaml
tools:
  generate_retry_message:
    rules:
      - action: allow
        rate_limit:
          max: 30
          window: 60

See the full Agentvet policy for all 3 tools.

Tool Name generate_retry_message
Category Write
MCP Server Agentvet MCP Server
Risk Level Medium

Agents calling write-class tools like generate_retry_message have been implicated in these attack patterns. Read the full case and prevention policy for each:

Browse the full MCP Attack Database →

Other tools in the Write risk category across the catalogue. The same policy patterns (rate-limit, validate) apply to each.

What does the generate_retry_message tool do? +

Given a tool name, validation error, and attempted args, build the canonical LLM-facing retry feedback message. Uses agentvet's ToolArgError.toLLMFeedback() formatting so the wording matches what runtime callers see.. It is categorised as a Write tool in the Agentvet MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on generate_retry_message? +

Add a rule in your Intercept YAML policy under the tools section for generate_retry_message. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Agentvet MCP server.

What risk level is generate_retry_message? +

generate_retry_message is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit generate_retry_message? +

Yes. Add a rate_limit block to the generate_retry_message 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.

How do I block generate_retry_message completely? +

Set action: deny in the Intercept policy for generate_retry_message. 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.

What MCP server provides generate_retry_message? +

generate_retry_message is provided by the Agentvet MCP server (@mukundakatta/agentvet-mcp). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Let agents act without letting them run wild.

Deterministic policy on every MCP tool call. Per-identity grants. Full audit log.

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