Skip to main content
Glama

preview_endpoint

FREE. See what a paid WebLens endpoint costs and what it returns before paying: the live price, a one-line summary, and a recorded sample of the exact response shape. Endpoints with no paid upstream (/fetch/basic, /contents, /map) also run a real truncated LIVE preview when you pass a url; SerpAPI- and Anthropic-backed endpoints return the recorded sample only, because free live runs there would burn upstream credits. Price: free

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoFetch-backed endpoints only (/fetch/basic, /contents, /map): run a real truncated preview of this URL
endpointYesPaid endpoint path to preview, e.g. "/answer"

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It is highly transparent: it states the tool is free, what it returns (price, summary, sample), that some endpoints run a real truncated live preview when a URL is provided, and that others only return a recorded sample due to upstream credit costs. This fully informs the agent about what to expect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively concise for the amount of detailed behavior it conveys. It is front-loaded with 'FREE' and the main purpose in the first sentence, then explains conditional behavior. The final 'Price: free' is redundant with the opening but not overly harmful. Overall, every sentence earns its place without unnecessary fluff.

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?

Given the tool's moderate complexity (conditional behavior based on endpoint type) and lack of output schema, the description is complete enough. It explains what is returned (price, summary, sample) and under what conditions a live preview occurs versus a recorded sample. An agent has sufficient information to select and invoke the tool correctly without ambiguity.

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?

The input schema already has 100% coverage with detailed descriptions for both parameters. The description adds some context about when the 'url' parameter is applicable and that the preview may be truncated, but this is largely redundant with the schema descriptions. It doesn't add significant new parameter-level meaning beyond what the schema provides.

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 the tool's purpose: to preview a paid WebLens endpoint before paying, showing the live price, a one-line summary, and a recorded sample of the response shape. It distinguishes itself from direct execution tools by emphasizing the 'before paying' aspect, making it clear this is a preflight/preview tool.

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 explicit guidance on when to use the tool: to see what a paid endpoint costs and returns. It also specifies conditional usage based on endpoint type — live preview for fetch-backed endpoints when a URL is passed, recorded sample only for SerpAPI/Anthropic-backed endpoints — with the reason (avoid burning upstream credits). It doesn't explicitly name alternatives but the contrast with actually calling the endpoint is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation2/5

Several tool clusters have near-overlapping purposes: fetch_webpage/fetch_webpage_pro/fetch_resilient and batch_fetch/get_contents are hard to distinguish, and answer_question/research/deep_research differ mainly in price and depth. The search_* and intel_* families are clearer, but the core fetching and research overlap creates ambiguity.

Naming Consistency3/5

Most tools follow a verb_noun snake_case pattern (fetch_webpage, search_web, extract_data), but there are notable exceptions like domain_intel, package_intel, youtube_transcript, memory_set, and intel_company, where the prefix/suffix convention is inconsistent. Still, the naming is broadly readable.

Tool Count2/5

35 tools is a large surface, far beyond the typical 3-15 range. The server covers many research verticals, but the number feels bloated, especially with multiple fetch and research variants that could be consolidated.

Completeness4/5

The tool set covers a wide range of web research needs: searching, fetching, crawling, extracting, screenshots, domain/tech/package intelligence, and market/competitive analysis. It lacks obvious lifecycle operations for monitors (list/delete/update) and memory (get/delete), but core workflows are well covered.