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massanaRoger

extracto-mcp

by massanaRoger

Extract structured data from a URL

extract
Read-only

Extract structured data from any public web page by providing a URL and a JSON schema; returns only matching fields as validated, typed JSON, with missing values as null.

Instructions

Extract structured data from a public web page and return it as validated, typed JSON. Extracto renders the page (JavaScript included), runs a schema-constrained extraction, and returns ONLY fields that match the schema. Missing data comes back as null rather than a hallucinated guess. Best for a single known URL. This call is synchronous (up to ~90s); for heavy or anti-bot pages prefer extract_async.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe public HTTPS URL to extract from.
schemaYesAn object mapping each field name to a type. A type is one of the literals "string", "number", "boolean", "array", "object"; OR a one-element array for a list (e.g. ["string"] for a list of strings, or [{ "title": "string", "price": "number" }] for a list of objects); OR a nested object (e.g. { "author": { "name": "string" } }). Use the most specific shape you can. Example: { "title": "string", "price": "number", "tags": ["string"], "reviews": [{ "user": "string", "stars": "number" }] }.
examplesNoUp to 3 few-shot examples to anchor the output format.
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint), the description discloses JavaScript rendering, schema-constrained extraction, return of only matching fields, null for missing data, and synchronous behavior with ~90s timeout. No contradictions.

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

Conciseness5/5

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

The description is concise at about 5 sentences, front-loaded with the main purpose, and each sentence adds essential information without redundancy.

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

Completeness5/5

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

Given there is no output schema, the description adequately explains return format (validated typed JSON), missing data handling, and execution constraints. All three parameters are thoroughly described in the schema, making the description complete for a tool of this complexity.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying that only schema-matching fields are returned and missing data is null, enhancing understanding of parameter behavior 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 the tool extracts structured data from a public web page and returns validated typed JSON. It distinguishes from sibling tool 'extract_async' by noting this is synchronous and best for a single known URL.

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

Usage Guidelines4/5

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

The description provides explicit context: 'Best for a single known URL' and recommends 'extract_async' for heavy or anti-bot pages. It does not address when to use other siblings like 'get_job' or 'list_jobs', but those have different purposes.

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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