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FetchV2 MCP Server

by praveenc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

fetch_batch

Fetch up to 10 webpages in one request and return combined content. Reduces round trips when comparing pages or gathering documentation context.

Instructions

Fetch multiple webpages in a single request and return combined content.

USE THIS TOOL WHEN:

  • You have 2-10 URLs to read (e.g., from discover_links results)

  • Comparing content across multiple pages

  • Gathering context from several documentation pages at once

KEY BENEFIT: One tool call instead of multiple fetch() calls = fewer round trips, faster results, and reduced overhead in supervised/approval workflows.

WORKFLOW EXAMPLE:

  1. discover_links(url="https://docs.example.com", filter_pattern="/api/")

  2. fetch_batch(urls=[...returned links...], max_length_per_url=1500)

NOTES:

  • Each URL's content is separated by '---' dividers

  • Failed URLs show inline errors without stopping other fetches

  • Robots.txt is NOT checked for batch fetches (assumes prior discovery)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesList of webpage URLs to fetch (max 10). URLs are fetched sequentially and results combined.
get_raw_htmlNoSkip content extraction and return raw HTML for all URLs.
max_length_per_urlNoCharacter limit per URL. Use 1000-1500 when fetching many pages. Default 2000.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and delivers: it discloses output formatting ('separated by '---' dividers'), error handling ('failed URLs show inline errors without stopping other fetches'), and policy caveat ('Robots.txt is NOT checked'). These are meaningful behavioral traits beyond the schema.

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 well-structured with clear sections (purpose, usage, benefit, workflow, notes) and front-loads the core purpose in the first sentence. Every section earns its place: the workflow example is actionable, and the notes disclose critical behavioral caveats.

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?

The description covers purpose, when-to-use, workflow, and behavioral notes. With a complete input schema and an output schema present, the description need not explain return values. It leaves no obvious gaps an agent would need to call the tool correctly.

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 100%, so baseline is 3. The description adds only marginal parameter guidance via the workflow example (max_length_per_url=1500), but does not explain parameter semantics beyond what the schema already states. It meets but does not exceed the baseline.

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 begins with a clear, specific verb+resource statement: 'Fetch multiple webpages in a single request and return combined content.' It explicitly distinguishes this tool from siblings like fetch (single URL) and discover_links (link discovery), and the '2-10 URLs' condition further clarifies its scope.

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

Usage Guidelines5/5

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

The description includes an explicit 'USE THIS TOOL WHEN' section with concrete conditions (2-10 URLs, comparing pages, gathering context), gives a workflow example linking discover_links to fetch_batch, and contrasts it with 'multiple fetch() calls'. This provides clear when-to-use guidance and implicitly identifies the alternative.

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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