Skip to main content
Glama

search_muscles

Find muscle IDs by searching muscle names to identify target muscles before locating exercises that work them.

Instructions

Search for muscles by name. Returns matching muscle IDs and names. Use this to discover muscle IDs before calling find_exercises.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (e.g. 'chest', 'bicep', 'quad')

Implementation Reference

  • src/tools.ts:165-183 (registration)
    The 'search_muscles' tool is defined and registered using server.tool in src/tools.ts. It takes a 'query' input and invokes client.searchMuscles(query).
    server.tool(
      "search_muscles",
      "Search for muscles by name. Returns matching muscle IDs and names. " +
        "Use this to discover muscle IDs before calling find_exercises.",
      {
        query: z
          .string()
          .min(2)
          .describe("Search query (e.g. 'chest', 'bicep', 'quad')"),
      },
      async ({ query }) => {
        try {
          const result = await client.searchMuscles(query);
          return { content: [{ type: "text", text: JSON.stringify(result, null, 2) }] };
        } catch (err) {
          return { content: [{ type: "text", text: formatError(err) }], isError: true };
        }
      },
    );
  • The actual implementation of the searchMuscles method that performs the network request/logic.
    async searchMuscles(query: string): Promise<unknown> {
      return this.request(`/api/v1/search/muscles?q=${encodeURIComponent(query)}`);
    }
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It successfully discloses the return format ('matching muscle IDs and names'), which is critical since no output schema exists. Does not mention error behaviors or pagination, but covers the essential output contract.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with zero waste: action statement, return value disclosure, and usage guideline. Information is front-loaded and logically ordered.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple search tool with one parameter and no output schema, the description is complete. It explains the return values and provides clear prerequisite guidance relative to siblings, which satisfies completeness requirements.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with the 'query' parameter fully documented (including examples like 'chest', 'bicep'). The description does not add additional parameter semantics, but with complete schema coverage, baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States specific verb ('Search'), resource ('muscles'), and scope ('by name'). The final sentence distinguishes this from sibling find_exercises by clarifying it returns IDs needed for that tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool: 'Use this to discover muscle IDs before calling find_exercises.' This directly addresses the workflow relationship with a sibling tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/csjoblom/musclesworked-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server