LLM Pricing Scraper MCP Server

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

No annotations are provided, so the description carries the full burden. It only says 'extract' and that it returns a JSON string, but does not disclose whether this is a read-only operation, what happens if the identifier is not found, or any rate limits or error conditions. Minimal disclosure of behavior beyond the basic action.

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 very short and to the point, with the main purpose front-loaded. No redundant phrasing or filler. It is concise without being under-specified.

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 an output schema, the description is minimal. It lacks context about the relationship with scrape_websites, any prerequisites, or potential failure modes. The output schema covers the return format, but the description does not tell the agent enough about when or why to use this 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?

Schema coverage is 0%, but the description compensates by explaining that 'identifier' can be a provider name, full URL, or domain. This adds meaningful semantics beyond the schema's bare 'Identifier' label, though it could be more specific about accepted 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?

States a clear verb (extract) and resource (scraped website), and the sibling scrape_websites is clearly different (scraping vs extracting). It does not specify what 'information' includes, but the purpose is unambiguous.

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?

No guidance on when to use this tool versus the sibling scrape_websites. The description does not mention prerequisites (e.g., that the website must have been scraped first) or any conditions that would make this tool the right choice.

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?

With no annotations, the description carries the full burden of disclosing side effects, limitations, or persistence. It only mentions that content is 'stored,' but does not elaborate on any I/O implications, error handling, or whether the operation is read-only. This is insufficient for an agent to anticipate the tool's behavior.

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 concise and to the point, using a single sentence to convey the primary action and then listing parameters without unnecessary verbosity. It is easily scannable and does not waste words, though it could be slightly richer without losing brevity.

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?

Given the complexity (nested objects, multiple parameters, and an output schema), the description is incomplete. It does not explain the semantics of the returned list of provider names, potential failure cases, or how the output relates to the input. The presence of a sibling tool also suggests a broader workflow that is not addressed, leaving the agent with gaps in understanding the tool's place in a larger process.

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 description adds minimal meaning beyond the schema. It restates that 'websites' is a dictionary of provider_name to URL, but does not explain the purpose of provider_name, the valid values for 'formats' (beyond examples), or the fallback behavior when 'api_key' is None. Since schema coverage is 0%, the description should have compensated but does not.

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 (scrape multiple websites) and the resource (websites), and mentions the use of Firecrawl and content storage. It is specific enough to understand the tool's primary function, though it could be clearer about how it differs from the sibling tool 'extract_scraped_info'.

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 provides no guidance on when to use this tool versus the sibling tool 'extract_scraped_info'. It does not mention that this tool is for raw scraping while the sibling is for extracting structured information, leaving the agent to infer the appropriate context without explicit instructions.

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