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agenson-tools

Web Content Extractor MCP Server

batch_extract

Process multiple URLs in parallel and return consolidated results. Enables batch analysis and comparison of web page content.

Instructions

Process multiple URLs in parallel and return consolidated results. Highly efficient for agents that need to analyze multiple pages, compare content, or do batch research. Includes rate limiting and error recovery.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesArray of URLs to process (max 10 for performance)
optionsNo
extraction_typeNoType of extraction to perform on all URLsarticle
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses key behaviors: parallel processing, rate limiting, error recovery, and configurable extraction type. It does not mention any destructive actions (likely read-only), and the error recovery ('continue_on_error' default true) is noted. Lacks details on output format but sufficient for a non-destructive 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?

Three sentences: core function, use cases, key features. No wasted words, front-loaded with action. Every sentence adds value.

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?

For a batch processing tool with three top-level parameters (one nested) and no output schema, the description covers the main functionality and key behaviors. It lacks explicit details on the consolidated result format but is sufficient for an agent to infer typical usage. Additional output schema would improve completeness.

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 high (67%+), so baseline is 3. The description adds context like 'highly efficient' and 'rate limiting' but does not significantly enhance parameter understanding beyond what the schema provides. The schema already describes each parameter's usage well.

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 'process multiple URLs in parallel and return consolidated results', specifying the action (process multiple URLs), resource (URLs), and output (consolidated results). It distinguishes from siblings like extract_article (single URL) by emphasizing batch processing and parallel execution.

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 usage context: 'Highly efficient for agents that need to analyze multiple pages, compare content, or do batch research.' It implies use for multi-URL tasks, implicitly contrasting with single-URL sibling tools. However, it does not explicitly state when not to use or name alternatives directly.

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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