Fetch normalized CoinGecko Learn article cards by category or limit. Use it to collect crypto guides, airdrop tips, and wallet or API articles as clean JSON.
CoinGecko Learn articles. Returns normalized article cards from CoinGecko Learn public pages.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Rows to return, default 20, max 50 | |
| category | No | Learn category, default all |
Changes observed during successful MCP inspections.
v1.17.5Input schema / properties / category / enumAdded value: +[
+ "all",
+ "latest",
+ "airdrop-guides",
+ "coins-and-tokens",
+ "guides",
+ "wallets-and-bridges",
+ "api",
+ "reviews"
+]v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the behavioral burden. It does disclose the output shape ('normalized article cards') and the source ('public pages'), which conveys read-only intent. But it doesn't mention pagination, ordering, rate limits, or what fields the cards contain, leaving a moderate gap for a no-annotation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences and gets to the point quickly, with the useful qualifier 'normalized article cards' in the second sentence. Minor redundancy: the first sentence largely restates the tool name, but it is not excessive.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with two documented optional parameters, the description gives a high-level return concept ('normalized article cards') but lacks details on output fields, pagination behavior, and how category filtering behaves. With no output schema and no annotations, an agent would need to infer some call semantics from the schema alone.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: limit and category each have meaningful descriptions plus an enum for category. The tool description adds no parameter-level meaning beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states a specific action ('returns normalized article cards') and resource ('CoinGecko Learn public pages'), which is clear and enough to distinguish from coingecko_news at a glance. However, it doesn't explicitly name or contrast with sibling tools, so it stops short of full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to choose this over coingecko_news or other Coingecko tools. The closest hint is 'CoinGecko Learn articles,' which implies the domain, but no explicit when-to-use, when-not-to-use, or alternative tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.