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Tavily

by io.github.pipeworx-io
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Extract

extract
Read-onlyIdempotent

Extract clean article text from one or more URLs via Tavily: strips boilerplate/navigation and returns up to 20,000 chars of readable content per page. Accepts a single URL string or an array. Ideal for feeding source pages into an LLM.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesA single URL string, or an array of URL strings, to extract clean text from.
_apiKeyNoOptional — your own Tavily API key for higher limits; omit to use the shared Pipeworx key.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "urls": "https://www.example.com/article"
+  },
+  {
+    "urls": [
+      "https://www.example.com/article1",
+      "https://www.example.com/article2"
+    ]
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent), the description adds concrete behavioral details: strips boilerplate/navigation, returns up to 20,000 characters per page, and accepts both single URL and array inputs. It also notes the API key fallback behavior. No contradictions with annotations.

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-loaded with the primary action, and every clause contributes value. No filler or repetition.

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 a simple read-only extraction tool with no output schema, the description covers input formats, processing behavior, output limits, and intended use case. This is complete for an agent to decide when and how to invoke it.

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 the description adds no meaningful parameter-specific information beyond what is already in the schema. The description mentions accepting a string or array, but this is already documented in the schema property description and examples.

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 and resource: 'Extract clean article text from one or more URLs via Tavily.' It clearly distinguishes from sibling tools like search and search_within by focusing on content extraction rather than search results.

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?

Provides clear context with 'Ideal for feeding source pages into an LLM,' which signals when to use. However, it does not explicitly mention when not to use or name alternative tools, so it stops short of full exclusion guidance.

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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