Medium Risk

share_result

Upload a benchmark result to the public MetriLLM leaderboard. Uses official upload defaults; METRILLM_* environment variables can override for self-hosted deployments. The resultFile must be an absolute path to a JSON file in ~/.metrillm/results/.

Part of the Metrillm server.

share_result can modify Metrillm data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use share_result to create or modify resources in Metrillm. 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 share_result repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Metrillm.

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

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "share_result": {
      "limits": [
        {
          "counter": "share_result_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Metrillm policy for all 4 tools.

Get this rule live on your own Metrillm server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access share_result gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so share_result only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the share_result tool do? +

Upload a benchmark result to the public MetriLLM leaderboard. Uses official upload defaults; METRILLM_* environment variables can override for self-hosted deployments. The resultFile must be an absolute path to a JSON file in ~/.metrillm/results/.. It is categorised as a Write tool in the Metrillm MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on share_result? +

Register the Metrillm MCP server in PolicyLayer and add a rule for share_result: 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 Metrillm. Nothing to install.

What risk level is share_result? +

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

Can I rate-limit share_result? +

Yes. Add a rate_limit block to the share_result 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.

How do I block share_result completely? +

Set action: deny in the PolicyLayer policy for share_result. 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 share_result? +

share_result is provided by the Metrillm MCP server (metrillm-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Metrillm tool call.

Deterministic rules across all 4 Metrillm tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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