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Glama

YouSpot

by com.youspot

Read web page

read_web_page
Read-only

Fetch and read the content of one public web page by URL — use when the user asks what a page or article says, or you need to read a prospect's or company's own site before a conversation. Reads the page as it renders in a real browser, so it works on JavaScript sites. Returns the page's readable text. Pass save=true to also keep the page in the user's graph as a web_link (searchable later); default is a one-off read that is not stored. Cannot read LinkedIn, social, or any sign-in-gated page — those are captured through the browser extension, and this tool says so rather than trying. Not for researching a company in general (that is research_company) or reading a file the user uploaded (that is read_file).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe http(s) URL of the page to read.
saveNoKeep the page in the graph as a web_link for later search. Default false — a one-off read is not stored.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses important behavior: it renders in a real browser for JavaScript sites, returns readable text, optionally saves the page as a web_link, defaults to a non-stored one-off read, and explicitly declines to attempt unreadable pages. This is substantial transparency that meaningfully exceeds annotation information.

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?

While longer than a simple one-liner, every sentence contributes: purpose, when to use, rendering behavior, return type, persistence option, limitations, and sibling differentiation. The most important information is front-loaded and there is no filler.

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?

For a simple two-parameter tool with no output schema, the description fully equips an agent: it explains what is returned (readable text), the persistence default, exclusions, and fallback alternatives. Nothing needed for correct invocation is missing.

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 coverage is 100%, so the schema already documents both parameters thoroughly. The description reinforces the save behavior with the graph/search context, but largely mirrors the schema descriptions rather than adding new parameter-level insight.

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: 'Fetch and read the content of one public web page by URL.' It clearly distinguishes itself from sibling tools by explicitly naming research_company and read_file as alternatives for different use cases.

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?

It gives explicit when-to-use guidance: 'use when the user asks what a page or article says, or you need to read a prospect's or company's own site before a conversation.' It also states exclusions (LinkedIn, social, sign-in-gated pages) and directs those cases to the browser extension, plus names non-alternatives.

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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TDQS

A3.8/5.0
Disambiguation4/5

Most tools are scoped to a distinct resource and action, and descriptions do a good job separating close pairs like search_connections vs ask_about_connections or get_my_linkedin_posts vs linkedin_analytics. However, the multiple deletion tools (delete_graph_object, delete_graph_objects, purge_graph_object) and the several file-reading tools are easy to confuse without reading the descriptions carefully.

Naming Consistency4/5

The vast majority of tools follow a clear verb_noun pattern such as create_, get_, list_, search_, send_, and delete_. A handful of noun-phrase outliers like linkedin_analytics, mutual_connections, top_message_correspondents, and what_needs_attention break the pattern, so it is highly consistent but not perfect.

Tool Count1/5

64 tools is an extreme count, far beyond the typical well-scoped 3-15 tool range and even beyond the 25+ threshold for 'too many'. While the server covers many integrations, this many tools creates a heavy navigation burden and would be better split into focused servers per domain.

Completeness3/5

Core graph/CRM operations and read-side integration coverage are strong, with search, get, list, and create tools across most domains. However, there are notable dead ends: no delete_calendar_event, no tracker management beyond create_tracker, and set_follow_up explicitly lacks a read-back query tool, so some natural user requests cannot be completed through the toolset.

Resources