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BotSpot: AI Trading, Backtests and Bots

by io.github.grzesir
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Check backtest status

backtest_status
Read-only

Fetch the latest status/stage for a backtest by id. Response includes stage plus a rich structuredContent.progress snapshot of the current heartbeat:

  • percent, eta, elapsed, simulationDate, portfolioValue, totalReturnPct, heartbeatElapsedSeconds

  • orders: array of open/recent orders from the simulation's current iteration, sanitized to the same shape as get_deployment_order_telemetry — the 7 canonical fields (timestamp, symbol, action, quantity, price, order_id, status, reason) PLUS flat option/contract metadata (asset_type, strike, expiration, option_type, order_type) surfaced from the nested asset dict when the strategy trades options/futures so callers can render e.g. "SPY 2024-01-24 465 PUT smart_limit" without parsing OSI themselves. null if the heartbeat omits orders or the payload was malformed. Handles both live-listener Order.to_dict() and backtest-heartbeat Order.to_minimal_dict() shapes.

  • positions: array of current holdings sanitized to the same essential-fields shape as get_deployment_position_telemetry (symbol, quantity, avg_fill_price, current_price, market_value, pnl, pnl_percent, asset_type, exchange, currency, multiplier, expiration, strike, option_type, underlying_symbol, asset). Accepts both full Position.to_dict() and compact Position.to_minimal_dict() (qty/val/pnl) shapes.

  • dataProvider: the provider slug the backtest is routing through (e.g. botspot_auto, ibkr, thetadata) so you can confirm routing mid-run without waiting for settings.json at completion. If you are monitoring, summarize those fields (progress + current results) instead of repeatedly polling.

Input Schema

NameRequiredDescriptionDefault
backtestIdYesActual backtest run UUID. Use the id returned by list_backtests or an explicit user-provided backtest id. Do not use a strategy id, aiStrategyId, revisionId, browser URL revisionId, conversation id, or a placeholder such as <unknown>.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

The readOnlyHint annotation already covers the safety profile, and the description goes well beyond it: it discloses that orders may be null when the heartbeat omits them or the payload is malformed, and that both Order.to_dict() and Order.to_minimal_dict() shapes are accepted. It stops short of discussing auth, rate limits, or cost of repeated polling, but the behavioral disclosure is unusually rich.

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

Conciseness3/5

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

The purpose is correctly front-loaded in the first sentence, but the body balloons into deep nested field enumerations and parenthetical asides (e.g., the worked 'SPY 2024-01-24 465 PUT smart_limit' example) that could be compressed. The detail is functional given there is no output schema, but the density hurts scanability.

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?

There is no output schema, so the description carries the full burden of explaining return values, and it does so exhaustively for stage, progress, orders, positions, and dataProvider, including shape variations. An agent knows exactly what it will get back and how to handle nulls.

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

Parameters3/5

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

Only one parameter and the schema description coverage is 100%, with the schema itself warning against strategy ids, revision ids, URLs, and placeholders. The description adds nothing further about backtestId, so the baseline 3 applies.

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

Purpose4/5

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

Opens with a specific verb and resource: 'Fetch the latest status/stage for a backtest by id,' which lets an agent distinguish this from artifact/log/visual siblings. It does not explicitly name which sibling to use for logs, charts, or dashboards, so the differentiation is implicit rather than stated.

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

Usage Guidelines3/5

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

Usage is implied by 'If you are monitoring, summarize those fields ... instead of repeatedly polling,' which hints at the polling/monitoring context. However, no alternatives are named (get_backtest_logs, get_backtest_visuals, show_backtest_dashboard, get_backtest_artifact) and there is no explicit when-not guidance.

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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