web-scraping-mcp-server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true and openWorldHint=true, so the safety profile is established. The description adds no additional behavioral context beyond the action itself—it doesn't mention potential slowness, site failure, or extent of crawling (e.g., same-domain only). No contradiction 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 sentences plus a brief args list—extremely concise with no filler. The main purpose is front-loaded in the first sentence.

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?

With no output schema, the description doesn't specify what the structured data looks like or how crawling behaves. It's sufficient for a simple 2-parameter tool, but leaves ambiguity about return format and crawl scope.

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 description coverage is 0%, so the description carries the burden. It explicitly explains each parameter ('Website URL to crawl' and 'Max pages to crawl') and the default, which goes beyond the bare schema. However, the explanations are minimal and don't elaborate on edge cases or valid formats.

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 ('Crawl') and resource ('a website'), clearly distinguishing from sibling search/lookup tools. It also indicates the output type ('structured data'). This is a clear, non-tautological purpose statement.

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 when to use the tool (when you want to crawl a specific website) but provides no explicit guidance on when to prefer alternatives like google_search or wikipedia_lookup. No exclusions or edge cases are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and openWorldHint, so the description needn't emphasize non-destructiveness. It adds value by specifying the output structure (titles, URLs, snippets), but lacks details on pagination, quotas, or failure modes.

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 extremely concise and front-loaded: one sentence states the tool's function, and a short Args block covers parameters. No wasted words.

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 2-parameter tool with no output schema, the description adequately explains what is returned. It lacks explicit error handling details or notes on result format, but the provided information is sufficient for basic invocation.

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?

Even though the input schema has no descriptions, the tool description fully explains both parameters: query (with an example) and max_results (with default value). This adds meaning beyond the schema's bare type declarations.

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?

Description states a specific verb (Search) and resource (Google), and clarifies it returns organic results with titles, URLs, and snippets. This clearly distinguishes it from sibling tools like crawl_website and wikipedia_lookup.

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 makes the use case clear: perform a Google search and obtain organic results. It doesn't explicitly exclude alternatives or provide when-not-to-use guidance, but the purpose is distinct enough to infer appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and openWorldHint. The description adds that it 'return[s] its content' but does not detail behavior such as search disambiguation, content format, or error handling. This is acceptable given the annotations but not rich.

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 lines, front-loaded with the primary purpose, and includes a minimal Args section. Every sentence earns its place with no unnecessary fluff.

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 one-parameter tool with annotations and a clear argument description, the description is mostly complete. The phrase 'return its content' is somewhat vague about output format, but it is sufficient for a basic lookup tool.

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 has zero description coverage for the 'topic' parameter, but the description compensates with an Args section explaining 'Topic to look up (e.g. 'Artificial intelligence')'. This provides clear meaning and a concrete example.

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 'Look up a Wikipedia article and return its content', which is a specific verb+resource combination. It distinguishes itself from sibling tools like crawl_website and google_search by being explicitly Wikipedia-focused.

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 for Wikipedia lookups but does not explicitly mention when to use it instead of crawl_website or google_search, nor does it provide exclusions or alternative recommendations. The guidance is implicit rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.