Download a web page and return its clean, readable text (no HTML or scripts). Let an agent read a URL's content pay-per-call. input=http(s) URL. [x402: 0.002 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | URL http(s) a leer |
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?
Since no annotations are present, the description carries the full burden. It clearly discloses that it performs a download, strips HTML/scripts, returns clean text, and is pay-per-use with a specific cost in USDC on Base. This gives the agent useful operational details beyond the bare tool name.
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 compact and front-loaded with the primary action. The cost information and URL format note are useful, though the 'Let an agent read a URL's content pay-per-call' phrase is slightly redundant with surrounding text. Overall, it is well-sized.
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?
Given the simple single-parameter input, no output schema, and straightforward read behavior, the description provides enough information for an agent to call the tool. It explains the core behavior, input format, and cost. It could add potential failure modes (e.g., password-protected pages), but not essential for basic use.
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?
The schema already documents the single 'input' parameter as an http(s) URL, so the description's repetition of 'input=http(s) URL' adds limited value. It does reinforce the URL format, but the param is already fully covered by the schema description.
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 clearly states a specific action and resource: 'Download a web page and return its clean, readable text (no HTML or scripts).' This distinguishes it from sibling tools like read-pdf, which targets PDFs, and makes it clear what the agent can accomplish.
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 usage context by stating it is for reading http(s) URLs and retrieving clean text. However, it does not explicitly compare to alternatives, mention conditions like 'use when you need textual content from a web page,' or provide when-not-to-use guidance. It is sufficient but not particularly directive.
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.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.