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Crawlora MCP

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coingecko_learn_articles

Fetch CoinGecko Learn article cards, filter by category, and set a limit to return titles, URLs, excerpts, authors, dates, and ratings.

Instructions

CoinGecko Learn articles. Lists article cards from CoinGecko Learn, optionally narrowed by category (all by default, latest, guides, reviews, api and others), up to limit (default 20, max 50). Each card has title, URL, image, category, excerpt, author, published date and, where present, rating score, text and votes.

Input Schema

NameRequiredDescriptionDefault
limitNoRows to return, default 20, max 50
categoryNoLearn category, default all

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / category / enum
      Added value: +[
      +  "all",
      +  "latest",
      +  "airdrop-guides",
      +  "coins-and-tokens",
      +  "guides",
      +  "wallets-and-bridges",
      +  "api",
      +  "reviews"
      +]
  2. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It discloses the return card fields (title, URL, image, category, excerpt, author, date) and caveats rating score/text/votes as 'where present', which is useful output-shape context. It says nothing about auth, rate limits, or pagination beyond the limit cap, leaving reasonable gaps.

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?

Two sentences, front-loaded with the resource and verb, then filtering and output shape. The field enumeration is a bit listy but each item is informative given there is no output schema. No filler.

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?

With no output schema and no annotations, the description sensibly compensates by enumerating the returned card fields and noting optional fields. Together with the complete input schema, an agent has enough to call and interpret it, though sibling routing and pagination behavior remain unspecified.

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 the schema already documents both parameters with their enums and defaults. The description restates category values (adding 'and others' beyond the enum) and limit's default/max, adding only marginal value. Baseline 3 is appropriate when the schema carries the detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: 'Lists article cards from CoinGecko Learn.' This is clear and, by naming 'Learn', distinguishes it from the sibling coingecko_news tool without explicitly declaring the contrast. Solid but stops short of the explicit sibling differentiation that would earn a 5.

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 implies how to narrow results via `category` and states the defaults, which gives implied usage guidance. However, it never says when to reach for this versus coingecko_news or coingecko_search, and there are no explicit exclusions. Usage is inferred, not directed.

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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