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Backtesting Arena

by io.github.Schoasch

Get Backtest Trades + Equity Curve

arena_get_backtest_trades

Which trades did that backtest actually take? Returns the individual round-trips of one of your backtest runs: entry and exit date, entry and exit price, per-trade P&L and the running equity after each trade. Use it when the aggregate metrics are not enough — to see whether a result rests on a handful of outlier trades, how long positions were held, or where the drawdown came from. Needs a run_id from arena_list_backtests; for the aggregates alone use arena_get_backtest. Closed round-trips only — a position still open at the end of the period is not counted. Raw trades are persisted for web-UI runs only: API/MCP-created runs return an empty list with an explaining empty_reason. [API Pro tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUUID of the backtest run.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / context
      Removed value: -{
-  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
-  "type": "string"
-}
    • changedInput schema / required
      Previous value: -[
-  "id",
-  "context"
-]New value: +[
+  "id"
+]
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral disclosure burden and does it well. It discloses that open positions are not counted, explains that raw trades are only persisted for web-UI runs and that API/MCP-created runs return an empty list with an empty_reason, and notes the API Pro tier requirement.

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 long but every sentence carries meaningful information: the output fields, use cases, prerequisites, the alternative tool, and caveats. It is front-loaded with an immediately understandable question, making it easy for an agent to grasp the tool's purpose quickly.

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 correctly enumerates the returned fields and explains edge-case behavior such as empty results. It also covers the run_id provenance, closed-trade limitation, and tier restriction, making the description sufficient for correct invocation.

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

Parameters4/5

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

The single id parameter is already fully documented in the schema with 100% coverage. The description adds practical value by telling the agent the id comes from arena_list_backtests, clarifying the expected provenance. This raises it slightly above the baseline.

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 the tool returns individual round-trip trades from a backtest run, listing entry/exit dates and prices, per-trade P&L, and running equity. It also distinguishes itself from the sibling arena_get_backtest by noting that that tool provides aggregate metrics alone.

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

Usage Guidelines5/5

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

It explicitly says when to use this tool ('when aggregate metrics are not enough') and names the alternative for aggregates ('use arena_get_backtest'). It also specifies the required prerequisite, a run_id from arena_list_backtests, and clarifies that only closed round-trips are included.

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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TDQS

A3.6/5.0
Disambiguation2/5

Many tools cover overlapping market indicators (e.g., cycle state, pulse, bullmarket ampel, volatility phases) and several share similar get_*_history patterns, which could cause an agent to select the wrong one. However, each tool has detailed descriptions with explicit references to related tools to reduce ambiguity.

Naming Consistency3/5

Tool names generally follow a verb_noun pattern (arena_get_*, arena_list_*, arena_run_*, arena_subscribe_*), but there are inconsistencies such as 'validate_strategy' (no arena_ prefix), 'arena_status' (not a clear verb_noun), and variations like 'arena_run_grid_backtest' vs 'arena_run_backtest'.

Tool Count2/5

With 84 tools, the server is heavily over-scoped for a typical MCP server, likely causing navigation and selection overhead. While the domain (crypto backtesting and analytics) is broad, 84 tools exceed reasonable coherence and impose a high cognitive load.

Completeness4/5

The tool surface covers a wide range of analytics (market indicators, backtesting, validation, alerts, subscriptions, reports) with few obvious dead ends. Minor gaps exist like lack of direct portfolio management or strategy editing, but core workflows are well covered.

Resources