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bastiaan365

mcp-it-ops

by bastiaan365

get_freqtrade_bot_status

Query a freqtrade bot's current trading state including open positions, profit/loss, and win rate via its REST API.

Instructions

Query a freqtrade bot's REST API for its current trading state.

bot_name must match a key under freqtrade.bots in config (e.g. 'steady', 'fun'). Reads basic-auth credentials from the env vars named in user_env / pass_env on the bot's config entry.

Returns a dict with: bot_name, dry_run, strategy, timeframe, exchange, closed_trade_count, open_trade_count, realised_p_and_l_fiat, realised_p_and_l_pct, total_p_and_l_fiat, total_p_and_l_pct, win_rate, open_positions (list of {pair, opened, current_pnl_fiat, current_pnl_pct}).

Returns {"error": "..."} on any failure (unknown bot, missing creds, API down).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bot_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses the return dict structure, error handling, and credential source. No side effects mentioned, but it's a read-only query, adequately transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise yet detailed, structured into purpose, config details, return format, and error handling. Front-loaded with the action, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (though not shown), the description compensates by detailing the return dict and error states. For a single-parameter tool with no annotations, this is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description thoroughly explains bot_name: it must match a key in config, with examples ('steady', 'fun'). This adds essential meaning beyond the empty schema.

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

Purpose5/5

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

The description clearly states it queries a freqtrade bot's REST API for its current trading state, with a specific verb and resource (query trading state). It distinguishes from sibling tools like get_backup_status by explicitly naming 'freqtrade bot'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: bot_name must match a key in config, credentials from env vars, and error handling. It does not explicitly contrast with alternatives, but the specificity makes usage clear.

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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