Fetch a public web page and return its readable text (title + extracted body, HTML stripped).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public http(s) URL to fetch | |
| max_chars | No | Max characters of extracted text, 500-20000 (default 4000) |
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?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It clearly explains the transformation (fetch, strip HTML, extract title and body) and the 'public' constraint, though it does not mention failure modes or handling of dynamic content.
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, front-loaded sentence with no wasted words. Every element adds meaning: the action, the target, and the output format.
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 two-parameter tool with a fully documented schema and no output schema, the description adequately explains the return format and constraints. Sibling tools are clearly distinct, so nothing essential is missing.
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%, so both parameters are already documented in the schema. The description adds only context about the output, not additional parameter-level meaning, so the baseline of 3 is appropriate.
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 resource ('public web page'), and precisely states the output ('readable text (title + extracted body, HTML stripped)'). This clearly differentiates fetch_url from sibling tools like analyze_text or generate_qr_code.
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 clear context: use this when you need the readable content of a public web page. It implies a contrast with siblings, though it does not explicitly name alternatives or state when not to use the tool.
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.
Each tool targets a different input type and operation: text analysis, URL fetching, QR generation, and image processing. There is no meaningful overlap, so an agent can confidently choose the right tool.
All tools follow the same snake_case verb_noun pattern: analyze_text, fetch_url, generate_qr_code, process_image. The naming is predictable and consistent across the server.
Four tools is a small but reasonable set for a general-purpose utility server. Each tool covers a distinct real-world use case, though the collection feels slightly minimal.
The individual tools are self-contained and have no obvious dead ends for their described operations. However, the tools are unrelated and the overall scope is unclear, so it is hard to assess whether the set is complete; common additions like PDF handling or OCR are noticeably absent.