freqtrade-mcp

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

Annotations indicate read-only, non-destructive, and idempotent behavior, which the description aligns with by describing a listing operation. The description adds valuable context beyond annotations: it specifies the return format ('List of DataFrame column entries with descriptions and contexts') and mentions the optional context filter with examples ('ohlcv', 'entry', 'exit', 'indicators'), enhancing transparency about output and filtering behavior.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by returns and args sections that are directly relevant. Every sentence earns its place by providing essential information without redundancy, structured clearly with labeled sections for Args and Returns.

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 the tool's moderate complexity (one optional parameter), rich annotations (readOnlyHint, idempotentHint, etc.), and the presence of an output schema, the description is complete. It explains the tool's purpose, parameter usage, and return format adequately, without needing to detail output values since an output schema exists. It covers all necessary context for effective use.

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 0% description coverage, so the description fully compensates by explaining the single parameter 'context' in detail: it's optional, can be 'ohlcv', 'entry', 'exit', or 'indicators', and if omitted returns all known columns. This adds crucial semantics not present in the schema, making it highly valuable for parameter understanding.

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: 'List common DataFrame columns available in strategy methods' with specific verbs ('List', 'Returns') and resources ('DataFrame columns', 'column names, types, and descriptions'). It distinguishes from siblings like 'freqtrade_list_strategy_methods' by focusing on column metadata rather than method listings.

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 clear context for when to use this tool: for getting column information in strategy methods like 'populate_indicators', 'populate_entry_trend', etc. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools, such as 'freqtrade_get_method_signature' for method details instead of column details.

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