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jfjensen

camofox-browser MCP server (stage2)

by jfjensen

extract

Fetch and extract structured data from web pages using a JSON schema. Returns clean JSON with the specified fields, bypassing manual parsing.

Instructions

Fetch a webpage and extract structured data from it according to a JSON Schema. The MCP server fetches the page, then asks a local Ollama model to populate the schema from the page contents. So the caller gets clean JSON back, without having to read or parse the snapshot itself.

Use this tool when the user asks for specific fields that you can name in advance, especially on pages with structured content (WHOIS lookups, product pages, GitHub repos, recipes, tables of data). Arrays and nested objects are supported, since the work is done by an LLM and not by a constrained server-side extractor.

Prefer fetch when the user asks an open-ended question or wants a free-form summary.

Args: url: The full URL to extract from. schema: A JSON Schema describing the fields to extract. Property descriptions guide the extraction model in finding the right page content. Example: { "type": "object", "properties": { "registrar": {"type": "string", "description": "Domain registrar name"}, "expiration_date": {"type": "string", "description": "Registrar Registration Expiration Date"}, "nameservers": {"type": "array", "items": {"type": "string"}, "description": "Name Server entries"} } } user_id: Optional, same semantics as for fetch.

Returns: A JSON string with the extracted fields. If a field cannot be found, it is set to null.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
schemaYes
user_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description explains the mechanism (fetches page, uses local LLM to populate schema) and return format. Missing details on error handling or limitations, but adequately transparent for a read operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well structured with key info front-loaded. Could be slightly more concise but no wasted sentences. Each sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers core functionality, usage, parameters, and return behavior (null for unfound fields). With output schema present, return details are less critical. Missing limitations but overall complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 0% description coverage, but the description fully compensates: explains `url`, gives detailed guidance and example for `schema`, and notes `user_id` semantics. Provides essential meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it fetches a webpage and extracts structured data per a JSON Schema. It distinguishes from siblings like `fetch` by noting the structured extraction use case.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (specific fields, structured content) and when to prefer the sibling `fetch` tool for open-ended questions. Provides clear context for tool selection.

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

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