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Scrapy MCP Server

Scrapy MCP Server

by ThreeFish-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

clear_cache

Remove all cached scraping results to force fresh requests in subsequent operations, ensuring updated and accurate data extraction with Scrapy MCP Server.

Instructions

Clear the scraping results cache.

This removes all cached scraping results, forcing fresh requests for all subsequent scraping operations.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "clear_cache"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses the destructive nature ('removes all cached scraping results') and the behavioral consequence ('forcing fresh requests'), but doesn't mention potential side effects like performance impact, permission requirements, or rate limits. It adequately describes the core behavior but lacks comprehensive operational context.

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 perfectly front-loaded with the core purpose in the first sentence, followed by a clarifying second sentence about consequences. Both sentences earn their place with no wasted words, making it highly efficient and well-structured.

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

Completeness4/5

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

Given the tool's simple nature (0 parameters, no annotations, but has output schema), the description is reasonably complete. It explains what the tool does and the immediate effect, though it could benefit from mentioning what the output schema returns. The presence of an output schema reduces the need to describe return values, but some indication of what to expect would enhance completeness.

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?

The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's purpose and behavior.

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 specific action ('Clear') and target resource ('scraping results cache'), distinguishing it from sibling tools that perform scraping, extraction, or monitoring operations. It precisely defines what the tool does without restating the name.

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 provides clear context for when to use this tool ('forcing fresh requests for all subsequent scraping operations'), but doesn't explicitly state when not to use it or mention alternatives. It implies usage in cache management scenarios but lacks explicit exclusions.

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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