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neuron_research_page

Deep-read a URL in one call: navigate, scroll, extract data, log console errors, and screenshot. Returns complete page understanding without multi-step chains.

Instructions

Deep-read a single URL in one call. Navigates to the page, waits for load, scrolls to trigger lazy content, extracts structured data, captures console errors, and takes a screenshot. Returns everything the AI needs to understand the page without multiple round-trips. Use instead of separate navigate → scroll → extract → screenshot chains.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to research
tabIdNoUse an existing tab (optional — opens a new tab if omitted)
scrollsNoNumber of scroll-downs to trigger lazy content (default: 3)
screenshotNoCapture a screenshot (default: true)
extractSelectorNoCSS selector to extract from (optional — auto-extracts if omitted)

Schema Changelog

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

  1. First observedv0.4.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does a solid job, disclosing that the tool navigates, waits for load, scrolls, extracts structured data, captures console errors, and takes a screenshot. It does not mention side effects like switching tabs, opening new tabs, or mutating page state, but the core behavioral profile is clearly communicated.

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 compact and front-loaded: the high-level purpose comes first, followed by a concise behavioral breakdown and a clear replacement directive. Every sentence earns its place with no filler.

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 tool's main phases and explains why it is useful. Since there is no output schema, a bit more specificity about the returned structured data could help, but the phrase 'Returns everything the AI needs to understand the page' sufficiently covers the intent for a research-oriented tool.

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 the schema already documents all five parameters well. The description adds contextual color around scrolling and screenshotting but does not provide parameter-level detail beyond that. Baseline 3 is appropriate.

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 opens with a specific verb and resource: 'Deep-read a single URL in one call.' It clearly distinguishes this composite tool from the individual step tools by naming the chain it replaces: navigate → scroll → extract → screenshot. The purpose is unambiguous.

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 explicit usage guidance: use this tool instead of separate navigate, scroll, extract, and screenshot calls. It conveys the key win (avoiding multiple round-trips) but does not state when the user should prefer the lower-level tools instead, such as when granular control over individual steps is needed.

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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