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Backfill Fetch

extract_data

Extract structured data from a web page, returned as JSON matching a JSON Schema you supply, via Backfill Extract. The service fetches the URL (a plain fetch first, with escalation when the plain fetch returns a bot wall or a JavaScript shell) and extracts the data described by the schema. Extraction is always a paid call (x402, USDC on Base: $0.15 fresh, $0.05 for a cached result), so a call over MCP returns an error directing you to the paid HTTP API at POST https://backfill.top/v1/extract; payment is never attempted over MCP.

Input Schema

NameRequiredDescriptionDefault
urlYes
modeNoauto
schemaYesA JSON Schema object describing the data to extract

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so unusually well: it discloses that every extraction is a paid call, the exact pricing ($0.15 fresh / $0.05 cached), that MCP calls return an error directing to the HTTP API, and that payment is never attempted over MCP. It also explains the fetch escalation path for bot walls and JS shells.

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?

Three dense sentences with the purpose front-loaded and no filler. The payment and error-redirect details are essential, though the pricing parenthetical is slightly heavy for a tool that will always error over MCP.

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?

For a tool whose actual MCP behavior is 'always error and redirect to the paid HTTP endpoint,' the description covers what an agent needs to know: cost, endpoint, and that payment won't happen over MCP. The only gap is the unexplained 'mode' enum, which matters since no output schema exists.

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

Parameters2/5

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

Schema coverage is only 33%. The description hints at the 'auto' mode's escalation behavior but never explains the 'plain' vs 'render' enum values, and adds nothing about the 'url' parameter. The 'schema' parameter is documented in the schema itself, but the mode parameter remains underspecified across both fields.

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?

States a specific verb and resource ('Extract structured data from a web page') plus the output contract ('JSON matching a JSON Schema you supply') and names the backing service. An agent immediately knows what this does and that it differs from a raw fetch.

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

Usage Guidelines3/5

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

The description explains the internal fetch strategy and the payment requirement, which implies the use case, but it never names or contrasts the 'fetch_page' sibling or states when to prefer it. Usage is inferable rather than explicit.

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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