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mcp-red-web-utils

mcp-red-web-utils

by hsharmanov02
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

reader

Extracts clean text from any web page URL, enabling AI agents to read online content without parsing HTML. Paid per call in USDC on Base via x402.

Instructions

Read any web page as clean text (paid per call, USDC on Base via x402)

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": "reader"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the critical behavior that each call is paid in USDC on Base via x402 — important for cost-aware agents. It says nothing about what happens on failure, whether JS-rendered pages are supported, rate limits, or how content is extracted, which are meaningful gaps for a paid fetch tool.

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?

One sentence, front-loaded with the core action and followed by the payment caveat. Nothing is wasted.

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?

For a one-parameter tool with no annotations and no output schema, the description covers purpose and billing, which are the essentials. It omits what the returned 'clean text' looks like in edge cases (paywalls, JS-heavy pages, errors) and whether payment happens before or after a failed fetch.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema gives only 'type: string, format: uri' for the single url parameter. The description implies the parameter is a web page URL ('Read any web page') but adds no constraints on scheme, redirects, or supported content types, so it only partially compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Read any web page as clean text'), which is unambiguous and clearly distinct from the financial/legal siblings (newpairs, edgar_search, caselaw_search). It doesn't explicitly name a sibling or contrast itself, but none of the siblings overlap, so the lack of differentiation is a minor gap.

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?

'Read any web page' implies the use case (fetch arbitrary URLs outside the structured data tools), but there is no explicit when-to-use, when-not-to-use, or alternative. An agent must infer that this is the fallback for arbitrary web content rather than a dataset query.

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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