Fetch a URL, return clean text. Free. Median 98% fewer tokens than raw HTML. Not for JS SPAs.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Page URL. | |
| headers | No | Headers to forward (Authorization, Cookie…). | |
| maxChars | No | Default 8000, max 32000. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / headers / descriptionAdded value: +"Headers to forward (Authorization, Cookie…)."Input schema / properties / maxChars / descriptionAdded value: +"Default 8000, max 32000."Input schema / properties / url / descriptionAdded value: +"Page URL."Input schema / properties / headersAdded value: +{
+ "type": "object"
+}Input schema / properties / maxChars / descriptionRemoved value: -"Max characters to return (default 8000, max 32000)"Input schema / properties / url / descriptionRemoved value: -"The URL to fetch and extract text from"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, and the description adds useful behavioral context: it is free, reduces tokens by 98% vs raw HTML, and is not suitable for JS SPAs. No contradictions with annotations.
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?
Two concise sentences deliver the core purpose, key benefit, and a limitation. No filler; every clause adds value.
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?
For a simple fetch-and-extract tool with fully specified parameters and a read-only annotation, the description sufficiently explains what it returns, its efficiency, and its main constraint. It is complete without needing an output schema.
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% with all parameters described in the input schema. The description adds no additional parameter semantics beyond the schema, so the baseline of 3 applies.
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 the function: 'Fetch a URL, return clean text.' It distinguishes from siblings like fetch_html by noting '98% fewer tokens than raw HTML' and sets scope with 'Not for JS SPAs.' This is a specific verb+resource+output.
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 gives an explicit when-not ('Not for JS SPAs') and implies usage for static pages needing clean text. However, it does not name alternative tools for SPAs or raw HTML, so it stops short of full guidance.
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.
Several tools overlap in fetching and processing web content (fetch_extract, fetch_html, fetch_metadata, html_to_markdown), which could confuse an agent. However, descriptions clarify output types, so most tools are distinguishable.
Names follow mixed conventions: verb_noun (fetch_html, remove_background), noun_verb (csv_query, rss_parse), and noun_noun (tool_catalog, screenshot_url). Each name is descriptive, but the lack of a consistent pattern makes it harder to guess tool names.
With 17 tools, the count is slightly above the ideal 3-15 range but still manageable. The inclusion of 5 meta-tools (pricing, tool_catalog, task_recipes, memory_snippet, use_tool) inflates the count but serves a discovery purpose.
The toolkit covers a broad range of web and data tasks (fetch, parse, query, convert, image, SEO). Minor gaps exist (e.g., no OCR, no image editing), but use_tool can dynamically access additional tools, mitigating incompleteness.