Which integrations are available for this server?

Allows using Brave Search as a search backend for web research, returning ranked passages from fetched pages. Uses DuckDuckGo as the default no-key search provider for web research. Supports Google Custom Search (google-cse) as a search provider for web research. Supports Ollama as a local embedding provider for on-device semantic ranking. Supports OpenAI embeddings for semantic passage ranking and can return results through OpenAI-compatible tooling. Supports Perplexity as a search provider for web research. Supports SearXNG as a search provider for web research. Supports Wikipedia as a search provider for web research.

How do I use WebVector MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@WebVector MCP Server Research the latest developments in renewable energy" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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WebVector MCP Server

by rthomas24

Overview Schema Source Related Servers Discussions Admin

TypeScript

Hybrid

WebVector

npm: webvector npm: webvector-mcp npm: webvector-cli License: MIT

Web research for AI agents in one call: search → read the full pages → rank → cited passages. No API keys, no model download, ~12 MB. Ships as an MCP server, a library and a CLI.

npx -y webvector-cli search "what changed in the MCP spec in 2026?"

**[1]** Streamable HTTP — Model Context Protocol — <https://modelcontextprotocol.io/specification/2026-07-28/…>
> Protocol versions 2025-03-26 through 2025-11-25 also used the Streamable HTTP transport, but in a
> different shape: servers could assign a session via the Mcp-Session-Id header … None of these
> mechanisms are part of this revision.
## Sources
- Streamable HTTP — Model Context Protocol — <https://…> [1]

WebVector CLI demo

Quick start: MCP server

Cursor, Claude Code, Claude Desktop, Windsurf, VS Code, Zed — paste into your MCP config (mcp.json / claude_desktop_config.json / .vscode/mcp.json):

{
  "mcpServers": {
    "webvector": {
      "command": "npx",
      "args": ["-y", "webvector-mcp"]
    }
  }
}

Or via Claude Code CLI:

claude mcp add webvector -- npx -y webvector-mcp

What you get: webvector_research (one-call web research with citations), webvector_fetch (read any URL), webvector_search (result list only), webvector_verify (citation checker), webvector_status (diagnostics). Zero config, no API keys required. Free OSS; you only pay upstream providers you opt into (Brave, Serper, OpenAI embeddings, etc. — DuckDuckGo is the default and requires no key).

Optional semantic tier: Add local ONNX embeddings (offline) or set OPENAI_API_KEY / VOYAGE_API_KEY / GEMINI_API_KEY — ranking upgrades from BM25 to hybrid automatically.

Related MCP server: MyWebSearch MCP

Run it

MCP server (Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, Zed …):

claude mcp add webvector -- npx -y webvector-mcp

{ "mcpServers": { "webvector": { "command": "npx", "args": ["-y", "webvector-mcp"] } } }

Library:

import { WebVector } from 'webvector';
const wv = new WebVector();
const res = await wv.research('reciprocal rank fusion k constant', { relatedQueries: ['RRF formula'] });
console.log(res.markdown);          // cited passages, ready for a prompt
console.log(res.evidence?.level);   // 'strong' | 'weak' | 'none' + suggestedQueries

CLI: npm i -g webvector-cli → webvector search "…" -k 8, webvector fetch <url> --query "…", webvector doctor.

Semantic tier (optional): npm i @huggingface/transformers (local ONNX embeddings, offline) or set OPENAI_API_KEY / VOYAGE_API_KEY / GEMINI_API_KEY … — ranking upgrades from BM25 to hybrid automatically. webvector doctor shows the active tier.

What it does

Capability	Example
One-call research — search, fetch every result (HTML, PDF, served Markdown), chunk, rank, cite	`wv.research(q)` · MCP `webvector_research` · `webvector search q`
Hybrid ranking that works keyless — BM25F (title/heading/body fields, proximity, identifiers like `AbortSignal.any`), vectors when available, relative-score fusion, per-source/domain diversity, adjacent-chunk merge	`retrieval.bm25.*`, `retrieval.fusion`, `retrieval.maxPerDomain`
Sub-questions covered — pass `related_queries`; xQuAD-style selection guarantees every aspect gets passages	`{ relatedQueries: ['UDP connectionless', 'TCP handshake'] }` → `res.coverage`
Evidence gate + follow-ups — LLM-free verdict (`strong / weak / none`), suggested queries, optional one in-call retry	`res.evidence`, `{ autoRetry: 1 }`, MCP `auto_retry`
Highlights, token budgets, deep links — best sentence per passage, packing into `max_tokens` with an explicit "N omitted" footer, `url#:~:text=` citations, `#page=N` for PDFs	`output.passageMode: 'highlight'`, `max_tokens`, `output.deepLinks`
Verify citations — classify each sentence of an answer as verbatim / paraphrase / unsupported against the cited passages; flags numbers not in the source	`wv.verifyCitations(answer, { sessionId })` · MCP `webvector_verify` · `webvector verify`
Read one page well — pagination (`start_index`), CSS `selector`, link lists, query-focused passages	MCP `webvector_fetch` · `wv.fetch(url, { selector })`
Fetch more pages, cleaner — markdown-first content negotiation (10–100× smaller on docs sites), fast paths (arXiv HTML, GitHub README/issues, Hacker News & Stack Exchange APIs, Google Docs), extractor ensemble with a recall guard, JS-shell detection (`PARSE_NEEDS_JS`) + optional render hook, `__NEXT_DATA__` recovery, boilerplate suppression	`ingestion.acceptMarkdown`, `ingestion.fastPaths`, `ingestion.html.strategy`, `ingestion.render`
Fast on repeat — SQLite page cache with ETag revalidation (second run: 0 requests), persistent embedding cache, single-flight, per-call `max_age_ms` / `cache_mode`	`~/.cache/webvector/pages.sqlite`, `webvector cache stats`
Sessions & stores — pages read once are reused across calls; memory / `sqlite` / Chroma / Qdrant / pgvector	`store.mode: session`, `store.provider: sqlite`
Providers — 11 search (DuckDuckGo default, Brave, Serper, Tavily, Exa, SearXNG …), 9 embedding, 5 rerankers, custom in one function	`docs/PROVIDERS.md`
Agent-ready MCP — namespaced tools, ≤2 KB instructions, concise/detailed output, `depth` presets, `objective`, sessions, `--max-uses` / `--allowed-domains` guardrails, `research` & `verify_claim` prompts; adapters for Anthropic (`search_result` blocks), OpenAI, Vercel AI SDK, LangChain	`packages/mcp`
Polite & safe — robots.txt + `Content-Signal`, per-host pacing, honest UA, SSRF guard, bot-wall detection (never retried), size/time caps, no telemetry, secrets redacted	`SECURITY.md`
Measured — offline eval over 32 recorded cases + 40-fixture extraction corpus run in CI; ranking changes are gated on it	`npm run eval` · `eval/`
Markets (opt-in) — ticker/market news from free feeds (deduped, event-tagged), SEC EDGAR filings + full-text search, macro/Fed/earnings calendar, StockTwits + FINRA short volume, VIX/yields pulse; every source classified open/feed/gray, gray off by default	`wv.markets.news(...)` · MCP `--tools markets` · `docs/MARKETS.md`

Why WebVector

	WebVector	Firecrawl	Jina Reader	Tavily / Exa	Playwright / Browserbase	Cursor/Claude WebSearch/WebFetch
Research pipeline	Search + fetch full pages + rank + cited passages in one call	Manual orchestration of crawl → LLM	Single-page read or search	Search only (no full pages) or API fetch	Manual browser scripting	Search returns snippets; fetch returns full page dump
Wedge	The finished research call, not a step	Deep site crawling (we don't)	Clean single-page markdown	Hosted search API (we can use as provider)	JS-heavy SPAs (we detect `PARSE_NEEDS_JS`, need render hook)	Built-in convenience; no ranking or citations
API keys	None (DuckDuckGo default); opt into providers	Required	Free tier, then key	Required	Required (+ browser infra)	Built into client (key implicit)
Runs where	Local Node process	Hosted service	Hosted service	Hosted service	Local or hosted browser	Client MCP or built-in
Output	Ranked cited passages, evidence gate, token budgets	Raw crawled content or LLM-processed	Clean markdown of one page	Search results with snippets	Full page content + JS state	Search snippets or raw HTML/markdown
Best for	Agents researching the live web with citations	Crawling entire sites, sitemaps, dynamic content	Reading one clean page	Hosted search when you need a key-based API	SPAs, forms, auth flows, screenshots	Quick built-in search or page fetch

Fair comparison: Firecrawl crawls sites (we don't); Jina Reader excels at single-page markdown (we use markdown-first content negotiation but focus on multi-page research); Tavily/Exa are hosted search we can use as providers; Playwright handles JS-heavy SPAs (we don't, unless you plug in a renderer); Cursor/Claude built-in tools are snippets-or-dump vs our ranked cited passages with evidence gating.

Configure

Zero config works. Otherwise webvector.config.yaml (with editor autocomplete via $schema) or WEBVECTOR_* env vars — every key in docs/CONFIGURATION.md. webvector init writes a starter file.

Docs

Full guide · Configuration · Providers · Markets · Architecture · MCP server · CLI · Security · Contributing · Eval

Develop

git clone https://github.com/rthomas24/web-vector && cd web-vector
npm install && npm run build && npm test && npm run eval

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One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained

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Resources

GitHub Repository

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