Lean Reader

lean_read

Fetch any URL and get token-minimized clean article text for LLMs, with a receipt showing token savings vs raw HTML.

Instructions

Fetch a URL and return token-minimized clean text plus a token-savings receipt. Strips nav/scripts/boilerplate so an LLM reads the article, not the page. The receipt counts tokens vs the raw page HTML (typically ~15x fewer, but it ranges from ~1.5x on already-clean pages to 100x+ on script-heavy docs). Two extractors (Defuddle + Readability), body-max selection, so it does not silently drop the article body. Static HTML only — JS-rendered pages may come back partial.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "lean_read"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	The URL to fetch and clean
`format`	No	Output format (default: markdown)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 18, 2026

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavior: it strips nav/scripts/boilerplate, uses two extractors with body-max selection, provides a savings receipt, and notes static HTML limitation. It even clarifies it does not silently drop the body, which adds trust.

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?

The description is a single paragraph of 4 sentences, efficiently covering action, stripping mechanism, receipt details, extractor method, and limitation. Every sentence adds value with no redundancy.

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

Completeness5/5

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

Given the tool's complexity (fetching, cleaning, token counting), the description covers main points: input URL, output clean text + receipt, extraction method, token savings range, and limitation for JS-rendered pages. No output schema is needed as the receipt is described.

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% with descriptions for both parameters, so baseline is 3. The description adds value by explaining the format enum values ('markdown' and 'text') and mentioning the token-savings receipt as part of the output, which goes beyond the schema.

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 core action: 'Fetch a URL and return token-minimized clean text plus a token-savings receipt.' It specifies the resource (URL) and the verb (fetch/return), and distinguishes what the tool does from alternatives by emphasizing token savings and boilerplate stripping.

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

Usage Guidelines4/5

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

The description implies usage context for reading article text without clutter and warns about JS-rendered pages, which guides when to use. However, it does not explicitly state when not to use or mention alternatives (though no siblings exist), so it lacks explicit when-not guidance.

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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