Add two numbers
AI agents invoke add to trigger actions in Example 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.
The tool performs arithmetic addition on two numbers. While this is likely a simple computation with no side effects, the description is minimal and does not explicitly confirm it is read-only or side-effect-free. Given sibling tools (divide, multiply, subtract) suggest a calculator-like server, this is almost certainly a pure computation tool.
From the tool's definition Tool name: 'add', description: 'Add two numbers'
Attacks that exploit this kind of access
Add two numbers. It is categorised as a Execute tool in the Example MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Example MCP Server MCP server in PolicyLayer and add a rule for add: 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 Example MCP Server. Nothing to install.
add 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 add 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 add. 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.
add is provided by the Example MCP Server MCP server (joseph19820124/joseph_mcp_server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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