scrape_page
scrape_pageFetch a web page and get clean content: title, headings, text, links. ~$0.005.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full page URL |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
scrape_pageFetch a web page and get clean content: title, headings, text, links. ~$0.005.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full page URL |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, and destructiveHint. The description adds the cost (~$0.005) and the 'clean content' behavior, which is useful, but it does not disclose potential side effects, failure modes, or limitations (e.g., handling of JavaScript-heavy pages). It adds some context beyond annotations but not extensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that packs in the action, resource, outputs, and cost. No filler or redundancy. It is optimally sized for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and an output schema exists, so the description need not explain return values. It adequately covers the core functionality, though it could mention what happens on errors or the meaning of 'clean' content. Overall, it's sufficient for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single 'url' parameter, which has its own description ('Full page URL'). The tool description does not add any additional semantic detail about the parameter, so it receives the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') and clearly states the resource (web page) and outputs (title, headings, text, links). This clearly distinguishes it from sibling tools like web_search (search) and call_endpoint (raw API calls), making its purpose immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case: when you need clean content from a specific URL. However, it does not explicitly state when to prefer this over alternatives or mention any exclusions. The context is clear, just not fully explicit about alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Four image generation tools, three video tools, and five 'ask' tools create significant overlap. Although descriptions specify the model, an agent must carefully compare prices and capabilities to choose correctly, making misselection likely.
All tool names use snake_case, but patterns are mixed: some start with verbs (remove_bg, scrape_page), some with nouns (crypto_prices, market_snapshot), and many use ai_/ask_ prefixes. Model suffixes like flux, gpt, pro, kling are descriptive but not systematically applied.
24 tools is heavy, inflated by near-duplicate variants for image, video, and LLM queries. While the broad scope justifies a large count, the redundant tools could have been consolidated.
The toolset covers a wide range of media and data tasks: image, video, music, voice, vision, LLM, web, crypto, domain, and endpoint discovery. Notable gaps like speech-to-text or image editing exist, but the surface is fairly complete for a general-purpose media toolkit.