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apm-install

Install packages on the Flux MCP server by specifying package name and process ID, enabling AI-powered code management without manual intervention.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageNameYes
processIdYes

Implementation Reference

  • src/mcp.ts:128-142 (registration)
    Registers the "apm-install" MCP tool, defining its schema (packageName: string, processId: string) and handler that delegates to installPackage function.
    this.server.tool(
      "apm-install",
      "install a package in an existing AO process",
      { packageName: z.string(), processId: z.string() },
      async ({ packageName, processId }) => {
        const result = await installPackage(
          packageName,
          processId,
          this.signer
        );
        return {
          content: [{ type: "text", text: cleanOutput(result) }],
        };
      }
    );
  • Core handler function for "apm-install". Constructs Lua code to call apm.install(packageName) and executes it in the target AO process via runLuaCode.
    export async function installPackage(
      packageName: string,
      processId: string,
      signer: any
    ) {
      const code = `apm.install("${packageName}")`;
      const result = await runLuaCode(code, processId, signer);
      return result;
    }
  • Zod schema defining input parameters for the "apm-install" tool: packageName (string) and processId (string).
    { packageName: z.string(), processId: z.string() },
  • Imports the runLuaCode helper used to execute the Lua installation command in the AO process.
    import { runLuaCode } from "../lib/runLua";
    
    export async function installPackage(
      packageName: string,
      processId: string,
      signer: any
    ) {
      const code = `apm.install("${packageName}")`;
      const result = await runLuaCode(code, processId, signer);
      return result;
Behavior1/5

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Tool has no description.

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

Conciseness1/5

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

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

Completeness1/5

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

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

Parameters1/5

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

Tool has no description.

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

Purpose1/5

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

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

Usage Guidelines1/5

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

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

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