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Diffbot

by io.github.pipeworx-io

Diffbot Extract

diffbot_extract
Read-onlyIdempotent

Extract the structured content (title, text, author) from — Diffbot analyzes any web page and returns its type, title, cleaned body text, author, publish date, site name, and language. Example: diffbot_extract({ url: "https://example.com/article", _apiKey: "your-token" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL to extract structured content from, e.g. "https://example.com/article"
_apiKeyYesDiffbot API token (free 10,000-credit tier, no card, at diffbot.com)

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-diffbot-api-key",
+    "url": "https://techcrunch.com/2024/01/15/ai-startup-raises-funding/"
+  }
+]
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

The description states it extracts and returns structured data, which aligns with readOnlyHint, openWorldHint, idempotentHint, and destructiveHint annotations. However, it adds no additional behavioral context beyond what annotations already provide, such as API rate limits or authentication details.

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?

Two sentences front-load the primary action ('Extract the structured content...') followed by an example. No superfluous words; every sentence adds value.

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?

Given the low complexity (2 parameters, no output schema) and strong annotations, the description is largely sufficient. It lists return fields and provides an example. Minor gap: no explanation of the return format, but it's reasonable for a simple extraction tool.

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 description coverage is 100%, so the schema already documents both parameters. The description includes an example but does not add new semantic information beyond the schema descriptions.

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 explicitly states the verb 'extract' and the resource 'structured content from a URL', listing specific fields returned. It clearly distinguishes from sibling tools like diffbot_company, which focuses on company data.

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 usage for extracting content from web pages but provides no explicit guidance on when to use this tool versus alternatives like diffbot_company or search_within. No exclusions or prerequisites are mentioned.

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

A3.6/5.0
Disambiguation2/5

Multiple research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and company analysis tools (entity_profile, compare_entities, recent_changes, bet_research) overlap heavily; the five polymarket_* tools also require careful reading to distinguish. Descriptions are detailed, but an agent must parse long disambiguation text to avoid mis-selection.

Naming Consistency2/5

Names mix verb-first (list_subscriptions, validate_claim, remember), noun-first (entity_profile, polymarket_arbitrage), product-prefixed (ask_pipeworx, pipeworx_feedback), and brand-prefixed (diffbot_company, diffbot_extract). No consistent verb_noun pattern across the set; polymarket_* and pipeworx_* prefixes are internally consistent but the overall scheme is chaotic.

Tool Count2/5

33 tools is well beyond the 3-15 sweet spot and in the 'too many' range. Many tools are meta-variants (4 ask_pipeworx flavors, 5 polymarket tools, 2 AI-visibility tools) that could be consolidated.

Completeness3/5

The data-research, prediction-market, memory, and subscription subdomains are each fairly complete at a meta level, with few dead ends. Gaps include no subscription update (delete + recreate required), no explicit memory update, and no direct way to execute an arbitrary discovered Pipeworx tool aside from routing through ask_pipeworx.

Resources