Analyze a webpage and extract its design system: colors, fonts, font sizes, spacing values, layout patterns (flexbox/grid usage), and component class name patterns. Uses static HTML fetch (does not render JavaScript).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to analyze |
| Name | Required | Description | Default |
|---|---|---|---|
| fonts | No | ||
| colors | No | ||
| layout_hints | 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 signal read-only and non-destructive behavior. The description adds a meaningful limitation—'does not render JavaScript'—which agents need to know before trusting the extracted design system. This goes beyond what annotations provide.
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 sentences, no filler. The core purpose and extracted items are front-loaded, and the critical rendering limitation is stated concisely at the end. Every part 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 read-only tool with an output schema and safety annotations, the description is largely complete. The only notable omission is explicit guidance to use the generic webscrape.scrape sibling when JavaScript rendering is required.
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% and the single parameter 'url' is already described as 'The URL to analyze'. The description adds no further semantic detail beyond referring to a 'webpage', 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 states a specific action ('Analyze a webpage and extract its design system') and enumerates concrete outputs (colors, fonts, font sizes, spacing, layout patterns, class names). This makes it clearly distinguishable from the sibling webscrape.scrape without needing to open the schema.
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 static-HTML caveat implies the intended context, but the description does not explicitly say when to use this tool versus webscrape.scrape or how to handle pages requiring JavaScript rendering. Usage is implied rather than directly guided.
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.