Scrape a webpage and extract its structure: title, headings, links, images, and text content. Uses static HTML fetch (does not render JavaScript).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to scrape |
| Name | Required | Description | Default |
|---|---|---|---|
| links | No | ||
| title | No | ||
| headings | No | ||
| text_content | 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?
Beyond the readOnly and non-destructive annotations, the description discloses an important behavioral limitation: it does not render JavaScript, so dynamically loaded content will be missing. This is valuable, non-obvious context that helps an agent set expectations. No contradiction with annotations was found.
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 two concise sentences with no fluff: the first states the action and expected outputs, and the second adds a critical limitation. It is front-loaded and every clause earns its place.
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 one-parameter scraping tool with an output schema and read-only annotations, the description is complete enough for an agent to invoke it correctly. It covers the input, the extracted content, and an important behavioral caveat, leaving no essential gap for this level of 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?
The schema already fully documents the single 'url' parameter with a clear description ('The URL to scrape'), and schema coverage is 100%. The tool description adds no additional parameter-level meaning beyond what the schema provides, matching the baseline for full 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 clearly states the action, resource, and expected output ('title, headings, links, images, and text content'), making it easy to understand what the tool does. It does not explicitly differentiate itself from the sibling 'webscrape.extractDesign', though the listed structure fields imply a distinction.
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 about the tool's capability by noting it uses static HTML fetch and does not render JavaScript, which tells an agent this is not suitable for JS-rendered pages. It does not explicitly state when to use this tool over alternatives like 'webscrape.extractDesign', so it stops short of full routing 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.
The tools are clearly namespaced by service (e.g., petstore-api, github-api, jsonplaceholder), which reduces cross-service confusion. Within each service, operations are generally distinct (e.g., getPetById vs updatePet). However, some overlap exists like updatePet and updatePetWithForm, and there are multiple 'get' tools across services that could be mixed up in a large set, but descriptions help.
Naming conventions are inconsistent across the set. Some tools use camelCase (github-api.getRepo), others use underscores (acme-mailer.send_email), and some are single simple verbs (echo-server.echo, memory.store). While each service follows its own style, the server as a whole lacks a unified pattern, making the naming chaotic.
With 38 tools, this server is heavily overloaded for a typical MCP scope. The tools span ten different services, indicating a broad aggregation rather than a focused purpose. This exceeds the recommended 3-15 tool range and even the 25+ threshold, making it feel like a collection of unrelated utilities.
The domain is unclear, but looking at each sub-service, most are incomplete. For example, github-api only offers read operations (no create/update/delete), jsonplaceholder has posts CRUD but only get for users, and open-weather lacks historical data. Memory and echo-server are trivial. The surface does not fully cover any single domain, leaving significant gaps for agent workflows.