get_defi_protocol
DeFi protocol breakdown with chain TVL. $0.02.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| api_key | Yes |
DeFi protocol breakdown with chain TVL. $0.02.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| api_key | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description adds only the cost ($0.02) but does not disclose behavioral traits such as read-only nature, required permissions, or side effects.
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 extremely short but lacks essential details, resulting in under-specification rather than efficient conciseness.
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?
No output schema, and the description only hints at 'chain TVL'. Given the complexity and number of sibling tools, this is insufficient for an agent to understand the tool's full behavior.
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 coverage is 0%, and the description provides no explanation for the parameters 'slug' and 'api_key'. The agent cannot infer what values to pass.
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?
The description mentions 'DeFi protocol breakdown with chain TVL', indicating it provides TVL per chain for a protocol. However, 'breakdown' is vague and does not distinguish from siblings like get_defi_tvl or get_defi_pools.
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?
No guidance on when to use this tool versus alternatives. With many sibling DeFi tools, this is a significant omission.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes with detailed descriptions. Minor overlaps exist (e.g., analyze_contract and get_meme_analyze both assess risk, but the former focuses on liquidity/honeypot while the latter is specifically for meme tokens), and tools like get_coin and get_price provide similar data but are differentiated by scope (detailed info vs real-time price). Overall, an agent can generally distinguish between them.
The naming convention is mostly verb_noun (e.g., analyze_contract, get_price, delete_alert), but there are inconsistencies such as 'register' (imperative verb alone) and 'trending_categories' (adjective_noun without verb). The mix of 'get_' prefix and bare verbs reduces consistency, but the pattern is still readable.
With 48 tools, the server is very heavy. While it aims to be a comprehensive crypto platform, this quantity can overwhelm an agent with too many options, making selection difficult and increasing latency. A more focused subset (e.g., 15-25) would be more appropriate for an MCP server.
The tool set covers an extensive range of crypto data and analysis: market overview, DeFi, memes, NFTs? (via meme scanning), portfolio, alerts, arbitrage, liquidations, whale tracking, social sentiment, network health, gas, and even agent management. It provides near-complete CRUD lifecycle for most data types (read, search, compare, analyze), leaving no obvious dead ends for a data-gathering agent.