boltwork-mcp

extract_tables

Extract all tables from PDFs into structured JSON. Returns headers, rows, and summary. Use for financial reports, research data, and invoices.

Instructions

Extract all tables from a PDF as structured JSON. Returns table count, headers, rows, and a summary. Costs 300 sats via Lightning. Use for: financial reports, research data, invoices with line items.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "extract_tables"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	URL of the PDF
`max_pages`	No	Max pages to process (default 20)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.22May 28, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses a cost of 300 sats via Lightning and the output format, but does not address authentication, side effects, or limitations beyond page count.

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?

Three sentences: purpose, cost, use cases. No filler, every sentence adds value, and it is front-loaded.

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?

The description covers purpose, output fields, cost, and use cases. For a simple two-parameter tool without output schema, this is nearly complete, though error handling and JSON structure specifics are not detailed.

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

Parameters3/5

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

Schema coverage is 100% and both parameters are well-documented in the schema. The description adds no additional parameter meaning beyond what the schema provides.

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 uses specific verb 'Extract' and resource 'tables from a PDF', and lists output fields (table count, headers, rows, summary). It clearly distinguishes from siblings like 'extract_data' and 'summarise_pdf' by focusing on tables.

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?

Explicit use cases are given: financial reports, research data, invoices with line items. However, it does not mention when not to use or explicitly compare with alternatives.

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

Deploy Server