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list_llms_txt_sources

Retrieve all source URLs for fetching llms.txt files to match specific technologies. Use fetch_llms_txt to access sources, and explore llms-full.txt or llms-mini.txt if needed. SushiMCP assists in context delivery for AI IDEs.

Instructions

This tool lists all available source urls where an llms.txt can be fetched. After reading the listed sources, use fetch_llms_txt to fetch any source that matches a technology in the instructions you received. Prefer llms.txt, but if llms.txt proves inadequate, check to see if other llms-full.txt or llms-mini.txt exist. When done, ask the user if they want to use other tools to search for documentation on any sources this tool could not find.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core handler function that lists configured llms.txt sources by formatting them into a text response.
    export const list_llms_txt_sources = async (
      extra: RequestHandlerExtra<ServerRequest, ServerNotification>,
      docSources: Record<string, string>
    ): Promise<CallToolResult> => {
      if (Object.keys(docSources).length === 0) {
        return {
          content: [{ type: "text", text: "No llms.txt sources configured." }],
        };
      }
      let formatted_sources = "Available llms.txt sources:\n";
      for (const name in docSources) {
        formatted_sources += `- ${name}: ${docSources[name]}\n`;
      }
      const content: TextContent[] = [
        { type: "text", text: formatted_sources.trim() },
      ];
      return { content };
    };
  • src/index.ts:107-112 (registration)
    Registers the tool with the MCP server, providing a detailed usage description and a handler wrapper that injects docSources from CLI args.
    server.tool(
      "list_llms_txt_sources",
      "This tool lists all available source urls where an llms.txt can be fetched. After reading the listed sources, use fetch_llms_txt to fetch any source that matches a technology in the instructions you received. Prefer llms.txt, but if llms.txt proves inadequate, check to see if other llms-full.txt or llms-mini.txt exist. When done, ask the user if they want to use other tools to search for documentation on any sources this tool could not find.",
      (extra: RequestHandlerExtra<ServerRequest, ServerNotification>) =>
        list_llms_txt_sources(extra, docSources)
    );
  • Declares the tool's metadata, description, and annotations in the server's capabilities for protocol compliance.
    list_llms_txt_sources: {
      name: "list_llms_txt_sources",
      description: "List the source urls where an llms.txt can be fetched.",
      annotations: {
        title: "List llms.txt sources",
        readOnlyHint: true,
        destructiveHint: false,
        idempotentHint: true,
        openWorldHint: false,
      },
    },
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the tool's behavior as listing sources and implies it's a read-only operation without side effects, but doesn't explicitly state safety aspects like whether it requires authentication, has rate limits, or what the output format looks like. It adds some context about workflow but lacks detailed behavioral disclosure.

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

Conciseness3/5

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

The description is front-loaded with the core purpose but includes verbose workflow instructions that extend beyond the tool's scope (e.g., 'ask the user if they want to use other tools'). While informative, some sentences could be trimmed for conciseness, as they describe post-tool actions rather than the tool itself.

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

Completeness4/5

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is fairly complete. It explains what the tool does, how to use it in context, and next steps. However, it lacks details on output format or error handling, which could be useful for an agent. The absence of an output schema means the description should ideally cover return values, but it doesn't.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't mention parameters, which is appropriate. Baseline is 4 for 0 parameters, as it doesn't need to compensate for any gaps.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'lists all available source urls where an llms.txt can be fetched.' It specifies the verb ('lists') and resource ('source urls'), but doesn't explicitly distinguish it from sibling tools like 'list_openapi_spec_sources' beyond the resource type difference. The purpose is specific and actionable.

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?

The description provides explicit guidance on when to use this tool and alternatives: it instructs to use 'fetch_llms_txt' after reading sources, mentions preferring 'llms.txt' but checking 'llms-full.txt' or 'llms-mini.txt' if inadequate, and suggests asking the user about other tools for missing sources. This covers when to use, next steps, and fallback options.

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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