calorie_burn

A read tool on the GadgetHumans API Hub MCP server.

SERVERGadgetHumans API Hub SOURCEpypi:gadgethumans-api-hub-mcp

Low RISK CLASS

Category Read

Parameters 00 required

Recommended Allowedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-scotia1973-bot-api-hub/calorie-burn.md

WHAT IT DOES

What calorie_burn does on GadgetHumans API Hub

AI agents call calorie_burn to retrieve information from GadgetHumans API Hub without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

RISK

Why calorie_burn is rated Low

Based on the name and context of the GadgetHumans API Hub (a utility API service for calculations and transformations), 'calorie_burn' most likely retrieves or computes calorie data without modifying any state. The lack of destructive, financial, or code-execution language, combined with the pattern of sibling tools (converters, checkers, ciphers), indicates a Read operation.

From the tool's definition Tool name 'calorie_burn' suggests a data retrieval or calculation function that computes calorie expenditure based on input parameters. No description provided, limiting direct evidence.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs calorie_burn safely

PolicyLayer is an MCP gateway: it sits between your AI agents and GadgetHumans API Hub, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For calorie_burn, this is the rule to start with:

calorie_burn Allowed

calorie_burn is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "calorie_burn": {}
  }
}

ALLOW ONLY THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect GadgetHumans API Hub, apply this rule, and every calorie_burn call is checked against it from then on.

FAQ

Questions about calorie_burn

What does the calorie_burn tool do? +

calorie_burn is a read tool on the GadgetHumans API Hub MCP server. It is categorised as a Read tool in the GadgetHumans API Hub MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on calorie_burn? +

Register the GadgetHumans API Hub MCP server in PolicyLayer and add a rule for calorie_burn: 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 GadgetHumans API Hub. Nothing to install.

What risk level is calorie_burn? +

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

Can I rate-limit calorie_burn? +

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

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

calorie_burn is provided by the GadgetHumans API Hub MCP server (pypi:gadgethumans-api-hub-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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