Find exercises that target a specific muscle, with optional filters.
AI agents call find_exercises to retrieve information from MusclesWorked without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This is a retrieval operation that queries an exercise database to search for exercises matching specified criteria (muscle target). The tool has no side effects beyond returning matching records. It fits the Read category as a search/list operation. Severity is low because misuse would only return unwanted exercise suggestions, with no harmful blast radius.
From the tool's definition Tool name 'find_exercises' and description 'Find exercises that target a specific muscle' indicate a search/query operation with no modification of data. No deletion, execution of external commands, financial transactions, or data mutations are mentioned.
Attacks that exploit this kind of access
Find exercises that target a specific muscle, with optional filters. It is categorised as a Read tool in the MusclesWorked MCP Server, which means it retrieves data without modifying state.
Register the MusclesWorked MCP server in PolicyLayer and add a rule for find_exercises: 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 MusclesWorked. Nothing to install.
find_exercises is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the find_exercises 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 find_exercises. 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.
find_exercises is provided by the MusclesWorked MCP server (musclesworked-mcp). 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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