Which integrations are available for this server?

Allows using Ollama (local models) as the language model to answer queries about webpage content by selecting relevant sections. Allows using OpenAI as the language model to answer queries about webpage content by selecting relevant sections.

How do I use wasp-mcp?

1. Click on "Install 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., "@wasp-mcp What are Andreas Klappenecker's research interests from his Texas A&M profile?" 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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wasp-mcp

by sfeeney1897

Overview Schema Related Servers Score Discussions

TypeScript

Remote

wasp-mcp

Web Agent Semantic Protocol — MCP Server

wasp-mcp is a Model Context Protocol server that lets Claude (or any MCP client) query arbitrary webpages with token-efficient, structure-aware retrieval. Instead of dumping raw HTML into the context window, WASP builds a lightweight structural index (the manifest) from a page's headings, then fetches content only for the sections relevant to a query.

The result: answers grounded in real page content at a fraction of the token cost of naive scraping.

See the WASP Whitepaper for full protocol specification.

How It Works

Every webpage has two useful layers:

Structure — headings and section anchors that form a table of contents. Small, cheap to index.
Content — the text under each heading. Expensive to send in full; most is irrelevant to any given query.

WASP exploits this split with a two-tier pipeline:

Tier 1 — get_manifest(url)
  ↓ Try GET /.well-known/wasp.json (site-native manifest, 3 s timeout)
  ↓ Fall back: fetch HTML → parse headings → generate manifest client-side
  → Returns: structured index (headings, anchors, depth, token estimates)

Tier 2 — fetch_chunk(url, anchor)
  ↓ Resolve anchor → DOM element (getElementById → querySelector → fuzzy match)
  ↓ Extract section text via Range API / heading-sibling walk
  → Returns: plain-text body of that section only

query_page(url, query)
  ↓ get_manifest → score chunks by keyword match → fetch_chunk for top results
  ↓ Build numbered [1. Heading] context → call Claude API → inline [N] citations
  → Returns: { answer, sources[] }

A naive full-page scrape of a typical faculty profile costs ~16,700 tokens. The same query via WASP costs ~2,700 — a 6× reduction.

Related MCP server: MCP Web Research Server

Install

Requirements: Node.js ≥ 18, an Anthropic API key.

git clone https://github.com/seanfeeney/wasp-mcp
cd wasp-mcp
npm install
npm run build

Set your API key:

export ANTHROPIC_API_KEY=sk-ant-...

Run the server (stdio transport, for Claude Desktop / Claude Code):

node dist/index.js

Add to Claude Code

Add wasp-mcp as a local MCP server in your Claude Code project config:

claude mcp add wasp -- node /absolute/path/to/wasp-mcp/dist/index.js

Or edit .claude/settings.json manually:

{
  "mcpServers": {
    "wasp": {
      "command": "node",
      "args": ["/absolute/path/to/wasp-mcp/dist/index.js"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Restart Claude Code after saving. Confirm the server is live:

/mcp

MCP Tools

`get_manifest`

Fetches the structural index for a URL. Tries the site's own /.well-known/wasp.json first; falls back to client-side DOM generation from the fetched HTML.

Parameters

Name	Type	Required	Description
`url`	string	yes	Fully-qualified URL of the page

Example

get_manifest("https://engineering.tamu.edu/cse/profiles/aklappenecker.html")

{
  "wasp": "1.0",
  "url": "https://engineering.tamu.edu/cse/profiles/aklappenecker.html",
  "title": "Andreas Klappenecker — Texas A&M CSE",
  "summary": "Faculty profile for Andreas Klappenecker.",
  "keywords": ["quantum computing", "cryptography", "image processing"],
  "chunks": [
    { "id": "chunk_001", "heading": "Andreas Klappenecker", "anchor": "#wasp-001", "depth": 1, "tokens": 5, "order": 1 },
    { "id": "chunk_002", "heading": "Research Interests",   "anchor": "#wasp-002", "depth": 2, "tokens": 4, "order": 2 },
    { "id": "chunk_003", "heading": "Selected Publications","anchor": "#wasp-003", "depth": 2, "tokens": 5, "order": 3 }
  ],
  "generated": "client"
}

`fetch_chunk`

Retrieves the plain-text body of a single section identified by its anchor. Anchor resolution uses a three-stage fallback: getElementById → querySelector → fuzzy heading match.

Parameters

Name	Type	Required	Description
`url`	string	yes	Page URL (used for cache lookup; re-fetches if not cached)
`anchor`	string	yes	CSS anchor string from the manifest (e.g. `"#research-interests"`)

Example

fetch_chunk(
  "https://engineering.tamu.edu/cse/profiles/aklappenecker.html",
  "#wasp-002"
)

Quantum computing, image processing, cryptography.

`query_page`

Full end-to-end retrieval: builds the manifest, scores chunks against the query, fetches relevant section bodies, calls Claude, and returns a cited answer.

Parameters

Name	Type	Required	Description
`url`	string	yes	Page to query
`query`	string	yes	Natural-language question
`provider`	string	no	`"claude"` (default) \| `"openai"` \| `"ollama"`

Example

query_page(
  "https://engineering.tamu.edu/cse/profiles/aklappenecker.html",
  "What are this professor's research interests?"
)

{
  "answer": "Professor Klappenecker's research interests are quantum computing [1], image processing [1], and cryptography [1].",
  "sources": [
    { "heading": "Research Interests", "anchor": "#wasp-002" }
  ]
}

Token Efficiency

Approach	Tokens sent to LLM	Example page
Raw HTML scrape	~16,700	TAMU faculty profile
WASP `query_page`	~2,700	same page, same query
Reduction	6.1×

Token savings grow with page length. A 50,000-token documentation page may see 20–40× reduction when only 2–3 sections are relevant.

Project Structure

wasp-mcp/
  index.ts        MCP server entry — registers tools
  manifest.ts     get_manifest() — discovery + DOM generation
  chunks.ts       fetch_chunk() — anchor resolution + text extraction
  retrieval.ts    query_page() — scoring, enrichment, LLM call
  providers.ts    claude / openai / ollama provider adapters
  cache.ts        In-memory URL → { manifest, html } cache with TTL
  types.ts        Shared TypeScript types

License

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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