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vybsly_search

Find comprehensive web content across 29M+ pages. Retrieve up to 30K characters per result with strict filters for research, news, or educational sources.

Instructions

Full-content web search across 29M+ pages. Returns up to 30K chars per result — perfect for RAG and agent context. Supports strict-mode filters (research/news/educational) and federation with encyclopedia.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (required)
limitNoMax results (1-50, default 10)
modeNoagent = structured output with key_facts and entities
strictNoEnforce filter allowlists instead of fuzzy matching
researchNoOnly research papers (arxiv, nature, pubmed)
newsNoOnly news outlets (Reuters, AP, BBC)
educationalNoOnly tutorials/docs (MDN, MIT OCW)
sourceNoRestrict to a specific domain, e.g. wikipedia.org
langNoLanguage filter (en, es, fr, de, ja, zh)
strict_fallbackNoAuto-retry relaxed when strict returns too few

Implementation Reference

  • index.js:34-53 (registration)
    Tool registration in TOOLS array with name 'vybsly_search', description, and inputSchema.
    const TOOLS = [
      {
        name: 'vybsly_search',
        description: 'Full-content web search across 29M+ pages. Returns up to 30K chars per result — perfect for RAG and agent context. Supports strict-mode filters (research/news/educational) and federation with encyclopedia.',
        inputSchema: {
          type: 'object',
          properties: {
            query: { type: 'string', description: 'Search query (required)' },
            limit: { type: 'number', description: 'Max results (1-50, default 10)', default: 10 },
            mode: { type: 'string', enum: ['default', 'agent'], description: 'agent = structured output with key_facts and entities' },
            strict: { type: 'boolean', description: 'Enforce filter allowlists instead of fuzzy matching' },
            research: { type: 'boolean', description: 'Only research papers (arxiv, nature, pubmed)' },
            news: { type: 'boolean', description: 'Only news outlets (Reuters, AP, BBC)' },
            educational: { type: 'boolean', description: 'Only tutorials/docs (MDN, MIT OCW)' },
            source: { type: 'string', description: 'Restrict to a specific domain, e.g. wikipedia.org' },
            lang: { type: 'string', description: 'Language filter (en, es, fr, de, ja, zh)' },
            strict_fallback: { type: 'string', enum: ['relaxed'], description: 'Auto-retry relaxed when strict returns too few' }
          },
          required: ['query']
        }
  • Input schema for vybsly_search: query (required), limit, mode, strict, research, news, educational, source, lang, strict_fallback.
    name: 'vybsly_search',
    description: 'Full-content web search across 29M+ pages. Returns up to 30K chars per result — perfect for RAG and agent context. Supports strict-mode filters (research/news/educational) and federation with encyclopedia.',
    inputSchema: {
      type: 'object',
      properties: {
        query: { type: 'string', description: 'Search query (required)' },
        limit: { type: 'number', description: 'Max results (1-50, default 10)', default: 10 },
        mode: { type: 'string', enum: ['default', 'agent'], description: 'agent = structured output with key_facts and entities' },
        strict: { type: 'boolean', description: 'Enforce filter allowlists instead of fuzzy matching' },
        research: { type: 'boolean', description: 'Only research papers (arxiv, nature, pubmed)' },
        news: { type: 'boolean', description: 'Only news outlets (Reuters, AP, BBC)' },
        educational: { type: 'boolean', description: 'Only tutorials/docs (MDN, MIT OCW)' },
        source: { type: 'string', description: 'Restrict to a specific domain, e.g. wikipedia.org' },
        lang: { type: 'string', description: 'Language filter (en, es, fr, de, ja, zh)' },
        strict_fallback: { type: 'string', enum: ['relaxed'], description: 'Auto-retry relaxed when strict returns too few' }
      },
      required: ['query']
    }
  • Handler implementation: calls Vybsly API /search endpoint with all parameters from args, returns JSON result.
    case 'vybsly_search':
      result = await vybslyCall('/search', {
        q: args.query,
        limit: args.limit || 10,
        ...(args.mode && { mode: args.mode }),
        ...(args.strict && { strict: 'true' }),
        ...(args.research && { research: 'true' }),
        ...(args.news && { news: 'true' }),
        ...(args.educational && { educational: 'true' }),
        ...(args.source && { source: args.source }),
        ...(args.lang && { lang: args.lang }),
        ...(args.strict_fallback && { strict_fallback: args.strict_fallback })
      });
      break;
  • Helper function vybslyCall() used to make HTTP requests to the Vybsly API, handling auth headers and error responses.
    async function vybslyCall(path, params = {}) {
      const qs = new URLSearchParams(params).toString();
      const url = `${VYBSLY_BASE}${path}${qs ? '?' + qs : ''}`;
      const headers = { 'Accept': 'application/json' };
      if (API_KEY) headers['X-API-Key'] = API_KEY;
      const res = await fetch(url, { headers });
      if (!res.ok) {
        const text = await res.text();
        throw new Error(`Vybsly API ${res.status}: ${text.slice(0, 300)}`);
      }
      return res.json();
    }
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses result size, strict-mode filters, and federation, but lacks details on rate limits, auth, error handling, or behavior of strict_fallback parameter.

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?

Two sentences efficiently cover core function, result size, and filter capabilities. No wasted words; information is front-loaded.

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

Completeness3/5

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

Given 10 parameters and no output schema, the description adequately covers purpose and major features but omits output format beyond char count, error handling, and integration details for federation.

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?

Schema coverage is 100%, so baseline is 3. The description adds contextual value by explaining 'agent' mode yields structured output with key_facts and entities, and groups filters conceptually. This exceeds mere schema repetition.

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?

The description clearly states the tool performs full-content web search across 29M+ pages, specifies result size, and highlights RAG/agent suitability. This distinguishes it from siblings like vybsly_ask or vybsly_knowledge.

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

Usage Guidelines3/5

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

The description implies use for RAG and agent context with high-character results, but does not explicitly state when to avoid this tool or list alternatives among siblings. Usage is implied but not fully contrasted.

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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