Skip to main content
Glama

web_extract_table

Read-only

Extract Table — Extract an HTML table from a public web page at a user-provided http(s) URL, as JSON rows or CSV. [category: web]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYeshttp(s) URL containing the table
outputNoBoth values return a JSON envelope: 'json' puts a rows matrix in 'rows'; 'csv' puts one CSV string in the 'csv' field — never a file.json
timeout_msNoOptional fetch timeout override in milliseconds.
user_agentNoOptional custom User-Agent header.
table_indexNo0-based table index
acknowledge_robotsNoBusiness+ only: proceed even when robots.txt disallows the page.

Schema Changelog

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

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety and external-access profile. The description adds the 'public web page' scope but does not disclose behaviors like robots.txt enforcement, failure modes, or what happens when no table is found; nor does it contradict the annotations.

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 core sentence is tight and front-loaded with the action and resource. Minor redundancy exists in the 'Extract Table —' prefix that duplicates the title, and '[category: web]' adds limited value, but overall the description is efficient.

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 6-parameter tool with no output schema, the description plus detailed parameter schema cover the required URL, output envelope, timeout, user agent, table index, and robots handling. The main gap is explicit guidance on sibling-tool selection and expected error behavior, but an agent can invoke the tool correctly from this definition.

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 each parameter is already well documented, including the JSON envelope behavior for CSV output. The description's 'JSON rows or CSV' adds no new detail beyond the schema, so the baseline 3 is appropriate.

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 states a specific verb and resource: 'Extract an HTML table from a public web page at a user-provided http(s) URL, as JSON rows or CSV.' It clearly distinguishes this from generic web_fetch and web_scrape_page siblings by targeting table extraction with explicit output formats.

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 implies use when a user wants a table from a public web page, and it signals limitations like 'public' and http(s)-only. However, it does not explicitly mention alternatives, exclusions, or when to prefer web_fetch/web_scrape_page, leaving routing partially to inference.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation2/5

Multiple tool pairs are near-identical: octopus_mkdir/octopus_make_folder and octopus_move/octopus_move_file are literal duplicates, analyze_hash/generate_hash both compute hashes, convert_word_to_pdf overlaps convert_document, and photo_compress/photo_compress_to_size plus pdf_thumbnails/pdf_to_images have fuzzy boundaries. The descriptions are detailed and cross-reference each other helpfully, but at 144 tools an agent will regularly misselect.

Naming Consistency3/5

The dominant {category}_{verb}_{object} snake_case pattern (pdf_*, photo_*, convert_*, analyze_*, media_*) is largely consistent and predictable. However, outliers like chatwithyourpdf and describe_image break the category-prefix convention, and the octopus namespace mixes bare verbs (read, write, mkdir) with verb_noun forms (make_folder, move_file, search_meta) inconsistently.

Tool Count2/5

144 tools is an extreme count for any MCP server. The broad scope (PDF, photo, video, audio, conversion, analysis, generation, file storage, web, e-sign) justifies some volume, but the count is inflated by batch and inspect variants (pdf_to_excel + batch + inspect), duplicate tools, and overlapping converters. An agent faces an overwhelming selection surface.

Completeness4/5

Per-domain coverage is remarkably deep: PDF spans merge/split/compress/protect/unlock/metadata/OCR/watermark and bidirectional conversion; photo covers editing, format conversion, face handling, OCR, and collage; file storage has full CRUD plus search. Minor gaps exist (no audio transcription, no video metadata editing, no deletion of PDF pages is actually covered via pdf_delete_pages) but the surface has no dead ends for its declared domains.

Resources