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yokingma

OneSearch MCP Server

one_extract

Extract structured data from web pages using LLM prompts and JSON schemas to organize information from URLs.

Instructions

Extract structured information from web pages using LLM. Supports both cloud AI and self-hosted LLM extraction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesList of URLs to extract information from
promptNoPrompt for the LLM extraction
systemPromptNoSystem prompt for LLM extraction
schemaNoJSON schema for structured data extraction
allowExternalLinksNoAllow extraction from external links
enableWebSearchNoEnable web search for additional context
includeSubdomainsNoInclude subdomains in extraction

Implementation Reference

  • Defines the Tool object for 'one_extract' including name, description, and input schema.
    export const EXTRACT_TOOL: Tool = {
      name: 'one_extract',
      description:
        'Extract structured information from web pages using LLM. ' +
        'Supports both cloud AI and self-hosted LLM extraction.',
      inputSchema: {
        type: 'object',
        properties: {
          urls: {
            type: 'array',
            items: { type: 'string' },
            description: 'List of URLs to extract information from',
          },
          prompt: {
            type: 'string',
            description: 'Prompt for the LLM extraction',
          },
          systemPrompt: {
            type: 'string',
            description: 'System prompt for LLM extraction',
          },
          schema: {
            type: 'object',
            description: 'JSON schema for structured data extraction',
          },
          allowExternalLinks: {
            type: 'boolean',
            description: 'Allow extraction from external links',
          },
          enableWebSearch: {
            type: 'boolean',
            description: 'Enable web search for additional context',
          },
          includeSubdomains: {
            type: 'boolean',
            description: 'Include subdomains in extraction',
          },
        },
        required: ['urls'],
      },
    };
  • src/index.ts:66-73 (registration)
    Registers EXTRACT_TOOL (one_extract) in the list of available tools for ListToolsRequest.
    server.setRequestHandler(ListToolsRequestSchema, async () => ({
      tools: [
        SEARCH_TOOL,
        EXTRACT_TOOL,
        SCRAPE_TOOL,
        MAP_TOOL,
      ],
    }));
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions LLM-based extraction and support for cloud/self-hosted options, but lacks critical details: what happens if extraction fails, rate limits, authentication needs, output format, or error handling. For a complex tool with 7 parameters and no annotations, this is a significant gap.

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

Conciseness4/5

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

The description is concise and front-loaded, stating the core purpose in the first sentence. The second sentence adds deployment context without redundancy. Both sentences earn their place, though it could be more structured (e.g., separating purpose from features).

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

Completeness2/5

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

Given the tool's complexity (7 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover behavioral aspects like extraction process, limitations, or output format, which are crucial for an LLM-based tool. Sibling tools suggest this is part of a suite, but no integration context is provided.

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?

The description adds minimal parameter semantics beyond the input schema. It mentions 'LLM extraction' which relates to the prompt/systemPrompt parameters, but doesn't explain how parameters interact (e.g., schema defines structure, prompt guides extraction). With 100% schema description coverage, the baseline is 3, and the description doesn't significantly enhance understanding of parameter roles or constraints.

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: 'Extract structured information from web pages using LLM.' It specifies the verb (extract), resource (structured information from web pages), and method (using LLM). However, it doesn't explicitly differentiate from sibling tools like one_scrape or one_search, which might have overlapping functionality.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'Supports both cloud AI and self-hosted LLM extraction,' which hints at deployment options but doesn't clarify use cases compared to siblings like one_scrape (likely for raw data) or one_search (likely for finding content). No explicit when/when-not statements are present.

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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