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by OBrandon
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

PDF tables to rows

pdf_tables
Read-onlyIdempotent

Extract tables from public text-based PDF URLs and return column names, row counts, rows, plus CSV and Excel links. Merges tables spanning pages for structured data use.

Instructions

Extract tables from PDF files at public URLs (text-based PDFs, not scans). Each item is one table: source PDF, pages, column names, row count, rows as objects (first 50 shown here), and links to the full table as CSV and Excel. Tables continuing across pages are merged. Cost: $2.00 per 1,000 tables on the user's Apify account (PDFs with no tables are free). Latency: usually 10-60 s depending on page count.

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": {
      "pdf_urls": "<pdf_urls>"
    },
    "name": "pdf_tables"
  }
}

Input Schema

NameRequiredDescriptionDefault
pagesNoOptional page range, e.g. '1-3,7'. Empty reads every page.
min_rowsNoIgnore tables with fewer data rows than this.
pdf_urlsYesDirect public links to PDF files (https://...). The file must download without a login.
strategyNo'lines' for tables with ruled borders, 'text' for tables aligned with spaces only.lines
max_tablesNoStop after this many tables.
pdf_passwordNoOptional. Password for protected PDFs. Leave empty otherwise.
merge_across_pagesNoJoin a table that continues on the next page.
first_row_is_headerNoUse the first row as column names.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/non-destructive annotations, it discloses concrete operational facts: $2.00 per 1,000 tables with no-table PDFs free, 10-60 s latency scaled by page count, and that tables spanning pages are merged. It also states the response shape and that only the first 50 rows are shown inline with CSV/Excel links for the full table, which is exactly the kind of context annotations cannot carry.

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, front-loaded with the core action and scope, then output shape, then cost and latency. No filler or repetition of the title, and each sentence contributes distinct information.

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?

For an 8-parameter extraction tool with an output schema, the description covers the remaining agent-critical unknowns: cost model, latency expectations, truncation of rows in the response, and cross-page merging. Nothing needed to call it correctly or decide whether to call it is missing.

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%, so every parameter including the 'lines' vs 'text' strategy enum is already documented. The description only reinforces the page-merging behavior tied to merge_across_pages and adds no syntax or format detail beyond the schema, which is the expected baseline when structured fields do the work.

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 gives a specific verb and resource ('Extract tables from PDF files at public URLs') plus a scoping qualifier ('text-based PDFs, not scans') that implicitly separates it from the image_tables sibling. An agent can identify the tool's job without opening the schema.

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?

Usage context is clear: public, login-free URLs and text-based (not scanned) PDFs, with a page-range and minimum-row knob mentioned in schema. It stops short of explicitly naming the alternative tool for scans or images, so the routing signal is implied rather than stated.

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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