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Crawlbase

by io.github.pipeworx-io

Crawlbase Structured

crawlbase_structured
Read-onlyIdempotent

Scrape a page into clean structured JSON using a named Crawlbase scraper — skip HTML parsing entirely. Common scraper names: "amazon-product-details", "amazon-serp", "google-serp", "facebook-page", "facebook-profile", "instagram-profile", "instagram-post", "linkedin-profile", "linkedin-company", "tiktok-profile", "ebay-product", "walmart-product-details", "github-repository", "generic-extractor". Full catalog: https://crawlbase.com/docs/scrapers/ — social-media scrapers (Facebook/Instagram/LinkedIn) work best with your JavaScript token. Example: crawlbase_structured({ url: "https://www.amazon.com/dp/1098145356", scraper: "amazon-product-details", _apiKey: "your-crawlbase-token" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe absolute URL of the page to scrape, e.g. "https://www.google.com/search?q=coffee" for google-serp.
_apiKeyYesYour Crawlbase token (JavaScript token recommended for social-media scrapers). Free tier at https://crawlbase.com
countryNoTwo-letter ISO country code to route through, e.g. "US", "DE". Matters for localized storefronts and SERPs.
scraperYesThe Crawlbase scraper name to apply, e.g. "amazon-product-details", "google-serp", "linkedin-profile". Catalog: https://crawlbase.com/docs/scrapers/

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-crawlbase-token",
+    "scraper": "amazon-product-details",
+    "url": "https://www.amazon.com/dp/B0C1H26C46"
+  },
+  {
+    "_apiKey": "your-crawlbase-token",
+    "country": "US",
+    "scraper": "google-serp",
+    "url": "https://www.google.com/search?q=coffee+shops"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's main addition is about the output format (structured JSON) and token recommendation. This adds worthwhile context without contradicting 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus an example, all front-loaded. The first sentence states the core action, the second explains scraper names and token advice, and the example solidifies understanding. No wasted words.

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 absence of an output schema, the description mentions 'clean structured JSON' but doesn't detail output per scraper. However, it provides a catalog link for that. The tool is well-explained for its purpose, and with 100% schema coverage and good annotations, the description is mostly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with decent descriptions. The description adds value by listing common scraper names and providing an example call, which helps agents understand parameter usage beyond the schema.

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 clearly states 'Scrape a page into clean structured JSON using a named Crawlbase scraper' and distinguishes it from raw HTML scraping by noting 'skip HTML parsing entirely'. It lists common scraper names and links to a full catalog, making the purpose very specific and differentiating it from siblings like crawlbase_scrape.

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?

The description advises when to use the tool (for structured data, no HTML parsing) and gives specific guidance for social-media scrapers (use JavaScript token). It provides a catalog link for detailed scraper selection. While it doesn't explicitly state when not to use it, the context of siblings and the mention of 'skip HTML parsing' implicitly suggests alternatives for raw content.

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

A4/5.0
Disambiguation4/5

The toolset is largely distinct: scraping, research, prediction-market, memory, and subscription tools each have clear boundaries. The ask_pipeworx family and the six Polymarket tools are closely related variants, but their descriptions provide explicit usage guidance, so an agent can select correctly with attention.

Naming Consistency3/5

Most tools use snake_case with descriptive names, but conventions are mixed: brand-prefixed noun phrases (crawlbase_scrape, polymarket_arbitrage, pipeworx_trending) sit alongside verb_noun tools (compare_entities, validate_claim) and bare verbs (remember, subscribe). The result is readable but not predictable.

Tool Count2/5

At 34 tools, the server spans several distinct domains (web scraping, structured data research, prediction markets, memory, subscriptions, feedback), making it feel like a kitchen sink rather than a focused toolset. The count is beyond the 'heavy' threshold and would benefit from splitting into separate servers.

Completeness4/5

The research surface is thorough: routing, grounded answers, deep research, entity profiles, comparisons, claim validation, and identifier resolution cover most real-world data needs. Minor gaps exist, such as no explicit tool to fetch pipeworx:// resource URIs and no crawler management for the scraping side.

Resources