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Fetch Page

fetch_page

Fetches a specified URL's full content using a persistent browser profile to handle login and JS-rendered pages, enabling deeper review of search results beyond snippets.

Instructions

searchの結果など、指定URLの本文をブラウザ経由(永続プロファイル使用)で取得する。

素のHTTP取得と異なり、ログイン状態やJSレンダリングが必要なページにも対応できる。 スニペットだけでは判断できない結果を、必要な数件だけ選んで深掘りする用途を想定。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
profileNodefault
headlessNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It explicitly states that fetching happens through a browser using a persistent profile, which implies cookie/login state and JS rendering support—useful behavioral context beyond the schema. It does not mention potential side effects on the profile or operational costs like slower fetches, but the core behavioral characteristic is well disclosed.

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 concise, with two focused sentences. The first sentence states the mechanism and main action, while the second adds the intended use case. No redundant wording or repetition of schema details.

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?

The description covers the main purpose, the intended workflow relative to search, and the key behavioral difference (browser, persistent profile). An output schema exists, so return values need not be described. The main completeness gap is the unaddressed headless/profile parameter semantics, but overall the description gives an agent enough context to select and invoke the tool effectively.

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%, so the description must compensate. It clarifies that URL is the target page and mentions the persistent profile concept, but it does not explain the 'profile' parameter's selectable values or what 'headless' means. The headless parameter is entirely unexplained, leaving an agent with an incomplete understanding of the available knobs.

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 states a specific verb and resource: it retrieves the body of a given URL via a browser with a persistent profile. It also distinguishes itself from plain HTTP fetching and positions itself as a deep-dive companion to search snippets, clearly separating it from sibling tools like search.

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 gives clear context for when to use this tool: for pages requiring login state or JS rendering, and for investigating a few search results not fully judgeable from snippets. It implies when not to use it by contrasting with plain HTTP fetching, though it does not explicitly name alternatives or exclusion conditions. This is strong contextual guidance, missing only an explicit 'when-not-to-use' statement.

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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