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extract

TYPED structured extract for autonomous agents — URL + schema → a clean, type-safe JSON record. Where /buy/fetch returns page TEXT (and ?extract= returns string-only fields), THIS returns the schema-conformant object an LLM/RAG/trading pipeline actually consumes: pass ?url=…&schema=title,price:number,rating:number,inStock:boolean and get back { title:"…", price:19.99, rating:4.5, inStock:true } — numbers as numbers, booleans as booleans, absent fields null (honest). schema accepts the URL-friendly compact form (field[:type], type in string|number|integer|boolean) OR a Firecrawl/OpenAI-style JSON-Schema object ({"properties":{"price":{"type":"number"}}}). That is Firecrawl's paid 'JSON mode' headline guarantee — type-safety, 'numbers as numbers not strings' — done DETERMINISTICALLY from the page's own JSON-LD/OpenGraph/meta/microdata: keyless, NO LLM call, NO API key, NO signup, $0.004/call, paid in-band over HTTP 402 (x402, USDC on Base mainnet). The typed record is folded into the SIGNED provenance attestation too (EIP-191, ecrecoverable OFFLINE), so a buyer can prove the EXTRACTED FIELDS — not just raw bytes — are exactly what MERCURY resolved. Honest charge-per-ATTEMPT: every call returns a structured result (success OR an ok:false reason). Same SSRF guard, 5s timeout, 10MB cap, no mint. — $0.004/call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesthe page to extract from (http/https)
formatNooptional: text (default) or markdown for the page-text field
schemaYesfields to extract. COMPACT: comma list of field[:type] (type in string|number|integer|boolean, default string), e.g. title,price:number,rating:number,inStock:boolean. OR a JSON-Schema string ({"properties":{"price":{"type":"number"}}}). Resolved from JSON-LD/OpenGraph/meta/microdata.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description fully carries the burden. It discloses deterministic extraction from page metadata, no LLM call, no API key, cost, payment method, signed provenance, SSRF guard, timeout, size cap, and that every call returns a structured result. 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.

Conciseness4/5

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

The description is long but well-structured, front-loading the core purpose and then providing detailed behavior, cost, and payment details. It could trim some peripheral details (e.g., USDC on Base mainnet) but remains valuable.

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 no output schema, the description thoroughly explains the return format (typed record with null for absent fields), input schema variants, extraction source, cost, timeout, SSRF guard, and comparison to siblings. It is fully complete for correct agent use.

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?

Schema coverage is 100% but the description adds significant value beyond the schema: it explains the compact schema format in detail, the alternative JSON-Schema object format, and clarifies the format parameter options. This greatly aids correct usage.

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 does typed structured extraction from a URL per a schema, returning a type-safe JSON record. It distinguishes itself from /buy/fetch by contrasting with their outputs, and the verb 'extract' directly matches the resource.

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?

It explicitly compares to /buy/fetch, stating when to use this tool (for schema-conformant objects) vs alternatives. It mentions the tool is deterministic, no LLM, no API key, but does not explicitly state when not to use it.

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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