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ZadenWorks

by io.github.ZadenGG

extract_tables

Extract every HTML table on a page as structured JSON — headers, row arrays, and (when headers exist and are unique) row objects keyed by header name, plus a best-effort caption for each table. Saves hand-parsing pricing tables, spec sheets, and schedules out of raw HTML. Paid: $0.01 per call in USDC on Base via x402 — call once without x_payment to receive the payment challenge.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic http/https URL to extract tables from
selectorNoOptional CSS selector to scope the search for tables to
maxTablesNo
x_paymentNoOptional X-PAYMENT header value from a completed x402 payment. Omit on first call to receive the payment challenge; complete the payment with your x402 wallet tooling, then retry with this set.
maxRowsPerTableNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.3/5.0
Behavior2/5

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

The description discloses the paid nature ($0.01) and the x402 payment challenge flow, but it does not mention behavior for pages with no tables, errors, or rate limits. More importantly, it claims 'every HTML table' while the maxTables parameter caps extraction at 20, creating a misleading expectation.

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 compact and front-loaded with the core purpose and output format. The payment instructions are necessary and clearly separated, though the 'Saves hand-parsing' clause is a minor value-prop addition rather than essential usage guidance.

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

Completeness3/5

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

It describes the output shape well (headers, row arrays, row objects, caption) and the payment workflow, but there is no output schema and no mention of edge cases like absent tables, malformed HTML, or what the first response (payment challenge) looks like. Given the tool's complexity, this leaves some gaps.

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?

Of the five parameters, the description only elaborates on x_payment; url, selector, maxTables, and maxRowsPerTable are left to the schema descriptions. Since schema coverage is moderate (60%), the prose should add more guidance on how these parameters affect extraction.

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 a specific verb ('Extract') and resource ('every HTML table') and clearly states the structured JSON output format. It distinguishes this tool from sibling tools like read_page or render_url by focusing on table extraction.

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?

It explains the x402 payment flow (call without x_payment to get a challenge, complete payment, retry), which is useful. However, it does not explicitly say when to use this tool versus alternatives such as read_page or render_url, aside from the implicit 'saves hand-parsing' value proposition.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, from SEO audits to link checking, content comparison, accessibility scans, structured data validation, and claim verification. No two tools overlap in function.

Naming Consistency4/5

Names mostly follow a consistent verb_noun snake_case pattern (audit_seo, check_links, compare_pages). Minor deviations like rank_x402_services and list_rank_categories are slightly less uniform but still readable and predictable.

Tool Count5/5

14 tools cover a broad but focused range of web page analysis and auditing workflows without feeling bloated. The count is well within the ideal 3-15 range for a specialized service.

Completeness5/5

The tool surface is remarkably complete for the domain: covers SEO, sitemaps, links, content, rendering, accessibility, structured data, claims, and monitoring. No critical dead ends or obvious missing operations for typical use cases.

Resources