pith

pith

Pith is an MCP server that enables LLM agents to scrape web pages to clean markdown, perform multi-page crawls, structured extraction with verifiable citations, and web search.

Category
访问服务器

README

Pith

The essential web, for agents. URL → clean markdown, crawl, structured extract, and search — with verifiable citations and deterministic request replay.

CI npm version license node

Pith is an open-source web-for-LLMs engine: point it at a URL and get clean, LLM-ready markdown — plus multi-page crawl, provider-agnostic structured extraction (with per-field confidence and citations you can verify), web search, and an MCP server. It runs with zero infrastructure and zero API keys by default: in-memory stores, an in-process queue, no database, no object store, no job runner. Postgres / MinIO / BullMQ / LLM providers are optional adapters behind explicit ports.

It's the core of a production web-for-LLMs platform, carved out as a library you can embed or self-host.

Install

npm install @use-pith/core
# optional peers, installed only if you use that face:
npm install fastify               # for @use-pith/core/http
npm install @modelcontextprotocol/sdk zod-to-json-schema  # for @use-pith/core/mcp
npm install playwright            # for the headless fetch tier

Quickstart (SDK, zero env)

import { createEngine } from "@use-pith/core";

const pith = createEngine();          // in-memory defaults — no keys, no containers

// Scrape: static → headless escalation, boilerplate stripped before Readability.
const page = await pith.scrape("https://example.com");
console.log(page.markdown);

// Crawl: same-domain bounded, dedup, worker-death resumable.
const handle = await pith.crawl("https://example.com", { maxDepth: 2, maxPages: 25 });
const status = await handle.wait();   // drains to a terminal status

// Structured extract + verifiable citations (needs a backend — see below).
const backend = createEngine({
  extractionBackend: createExtractionBackend({
    baseUrl: process.env.EXTRACTION_BASE_URL!,
    apiKey: process.env.EXTRACTION_API_KEY!,
    model: process.env.EXTRACTION_MODEL!,
  }),
});
const { data, confidence, citations, flaggedFields } = await backend.extract(
  "https://example.com",
  { type: "object", properties: { title: { type: "string" } }, required: ["title"] },
);

createEngine() with no arguments works immediately. extract and search throw NotConfiguredError until you pass a backend — Pith never assumes a billable provider.

The three faces

Face Import What it is
SDK @use-pith/core createEngine() — call scrape / crawl / extract / search in-process.
HTTP @use-pith/core/http createServer({ engine }) — a Fastify app exposing /health + /v1/scrape, /v1/crawl, /v1/crawl/:id, /v1/search, /v1/extract. See docs/openapi.yaml.
MCP @use-pith/core/mcp buildMcpServer({ engine }) — 5 tools: scrape, search, crawl, get_crawl_status, extract. Speaks the MCP protocol to any client.
// HTTP
import { createEngine } from "@use-pith/core";
import { createServer } from "@use-pith/core/http";
const app = await createServer({ engine: createEngine() });
await app.listen({ port: 3000, host: "127.0.0.1" });

// MCP
import { buildMcpServer } from "@use-pith/core/mcp";
const server = await buildMcpServer({ engine: createEngine() });
// …connect the SDK's StreamableHTTPServerTransport (see examples/mcp/)

Use it from any MCP client (e.g. Claude Desktop)

buildMcpServer({ engine }) returns a standard MCP server — connect it to any transport. The simplest is a small stdio bridge (examples/mcp/server.ts):

// examples/mcp/claude_desktop_config.json  (run from the repo root, or adjust the path)
{
  "mcpServers": {
    "pith": { "command": "npx", "args": ["tsx", "./examples/mcp/server.ts"] }
  }
}

The 5 tools (scrape, search, crawl, get_crawl_status, extract) then appear in the client. Full setup in examples/mcp/.

Configuration

Everything is optional. Pith ships no default billable endpoint.

Env var Face / feature Notes
EXTRACTION_API_KEY extract Required to enable structured extraction.
EXTRACTION_BASE_URL extract OpenAI-compatible endpoint. The accuracy benchmark defaults to https://api.openai.com/v1.
EXTRACTION_MODEL extract e.g. gpt-4o-mini.
BRAVE_API_KEY search Required to enable the Brave search backend.
freshnessTier (per-request) scrape Opt into stale-while-revalidate caching ("news" / "standard"); see docs/ARCHITECTURE.md.

See .env.example for the full list (zero required).

Why Pith

  • Defense-in-depth content cleaning — boilerplate is stripped before Readability runs, so the reader sees real article text, not nav chrome.
  • Provider-agnostic, verifiable extraction — every field carries a confidence score (FR-4) and a citation; citations are independently re-verified against the page's own text (FR-8), so hallucinated quotes are caught, not just claimed against. Works against any OpenAI-compatible endpoint.
  • Tier escalation — static fetch first, headless only when the result is thin or the static fetch fails; a budget ceiling can decline an escalation that has a cheaper fallback.
  • Deterministic request inspection / replay — every request is snapshot-able and replayable.
  • Opt-in freshness — stale-while-revalidate scrape cache with a monotonically-tightening tier model.
  • Test-first quality gate — a 5-project Vitest matrix (unit / smoke / integration-real / integration-nock / accuracy) stays green key-free on every PR; the 20-fixture extraction accuracy benchmark runs nightly (key-gated), hard-asserting ≥90% field accuracy and ≥90% citation verification against real model output.

Architecture

Pith is one core, three faces. Every host concern (cost metering, request snapshots, crawl state, content blobs, the job queue, robots resolution, the freshness cache, the clock) is an injectable port with an in-memory / no-op default. Swap in real adapters without touching engine logic.

                 ┌──────────── createEngine() ────────────┐
   SDK ─────────▶│ scrape · crawl · extract · search      │
   HTTP ────────▶│ (one request-handling core, shared)    │◀─▶ CorePorts (8 ports, null defaults)
   MCP  ────────▶│                                        │     └ real adapters optional (PG/MinIO/BullMQ/…)
                 └────────────────────────────────────────┘

Read docs/ARCHITECTURE.md for the port table, the "no infrastructure on import" invariant, and the carve-out boundary.

Examples

Contributing

See CONTRIBUTING.md. Quick rules enforced in CI: no infrastructure on import, test-first (the matrix stays green key-free), no secrets / no real crawled HTML. Security issues: see SECURITY.md — do not open a public issue.

License

Apache-2.0 — see LICENSE.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选