Tatum MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions '[fee_estimation] 1 credit per API call,' which hints at cost and operation type, but lacks details on permissions, rate limits, error handling, or response format. For a tool with no annotation coverage, this is insufficient to fully inform the agent.

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 moderately concise but could be better structured. It front-loads the purpose but includes verbose details like fee units and blockchain list in a single sentence, making it slightly cluttered. While not wasteful, it lacks optimal clarity and flow for quick comprehension.

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?

Given no annotations and no output schema, the description is incomplete. It covers the basic operation and parameters but omits critical behavioral aspects (e.g., response format, error cases) and usage context. For a tool with 1 parameter and no structured output, more detail is needed to adequately guide an AI agent.

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?

The input schema has 100% description coverage, clearly defining the 'chain' parameter with an enum. The description adds minimal semantics by listing supported blockchains (Bitcoin, Dogecoin, Ethereum, Litecoin), which loosely maps to the enum values. However, it does not explain parameter interactions or usage beyond what the schema provides, resulting in a baseline score.

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 clearly states the tool's purpose: 'Get the recommended fee/gas price for a blockchain.' It specifies the verb ('Get') and resource ('fee/gas price'), and includes scope details like fee units and supported blockchains. However, it does not explicitly differentiate from sibling tools (e.g., 'estimate_fee_blockchain'), which prevents a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It lists supported blockchains but does not mention sibling tools like 'estimate_fee_blockchain' or explain use cases, prerequisites, or exclusions. This lack of comparative context limits its utility for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.