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Deep Research MCP Server

Deep Research MCP Server

by SECRET4422
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

scrape_page

Scrapes a webpage URL and converts its content to structured text formats like markdown, with options to extract only the main content for cleaner output.

Instructions

Scrape URL to markdown

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": "scrape_page"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYes
formatNomarkdown
extractMainOnlyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.3.1
    • removedInput schema / properties / extractMainOnly / description
      Removed value: -"Try to extract main article content only, removing nav/footer"
    • removedInput schema / properties / format / description
      Removed value: -"Output format"
    • removedInput schema / properties / url / description
      Removed value: -"URL to scrape"
  2. First observedv1.2.0

TDQS

C2.5/5.0
Behavior2/5

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

With no annotations, the description carries full burden but only states the core action. It does not disclose behavioral traits such as whether the scraping is destructive, network requirements, rate limits, or error handling, leaving much to assumption.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While extremely concise (one sentence), it lacks structure such as bullet points or sections. The brevity results in under-specification for a tool with three parameters, making it more insufficient than appropriately concise.

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

Completeness2/5

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

Given the lack of output schema, no parameter explanations, and no annotations, the description is incomplete. It does not cover return values, error scenarios, or behavioral nuances needed for an AI agent to use the tool correctly.

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

Parameters2/5

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

Schema description coverage is 0%, yet the description adds no meaning to the three parameters (url, format, extractMainOnly). It hints at output format via 'markdown' but does not explain any parameter semantics, failing to compensate for low coverage.

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?

The description 'Scrape URL to markdown' clearly states the verb (scrape) and resource (URL), and specifies the output format (markdown). It implicitly distinguishes from siblings like search_web and fact_check_claim, though no explicit differentiation is made.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives, prerequisites, or scenarios where it should be avoided. The description is purely functional with no usage context.

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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