Low Risk

status_leaderboard_free

Cross-provider uptime leaderboard, ranked by uptime % DESC. Free, 7-day cap. Computed from minute-resolution counters (~720 samples per provider per day). For 90-day windows plus incident_count and mttr_minutes per provider use status_leaderboard (1 credit).

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

AI agents call status_leaderboard_free to retrieve information from TensorFeed without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though status_leaderboard_free only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

tensorfeed.yaml
tools:
  status_leaderboard_free:
    rules:
      - action: allow

See the full TensorFeed policy for all 42 tools.

Tool Name status_leaderboard_free
Category Read
Risk Level Low

View all 42 tools →

Agents calling read-class tools like status_leaderboard_free 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 Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.

What does the status_leaderboard_free tool do? +

Cross-provider uptime leaderboard, ranked by uptime % DESC. Free, 7-day cap. Computed from minute-resolution counters (~720 samples per provider per day). For 90-day windows plus incident_count and mttr_minutes per provider use status_leaderboard (1 credit).. It is categorised as a Read tool in the TensorFeed MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on status_leaderboard_free? +

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

What risk level is status_leaderboard_free? +

status_leaderboard_free is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit status_leaderboard_free? +

Yes. Add a rate_limit block to the status_leaderboard_free 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 status_leaderboard_free completely? +

Set action: deny in the Intercept policy for status_leaderboard_free. 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 status_leaderboard_free? +

status_leaderboard_free is provided by the TensorFeed MCP server (@tensorfeed/mcp-server). 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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