Anansi Haven

Trade Postmortem & MFE/MAE Rule-Based Grader

trade_postmortem

Read-onlyIdempotent

FREE rule-based trade grade. Trade postmortem & MFE/MAE excursion expectancy analyzer: scores entry/exit efficiency, stop-loss placement vs empirical symbol ATR/MFE/MAE benchmarks, R-multiple capture, and rule compliance (rollover window, stop floor, time-stop) using fixed rules (no AI model calls); the paid /v1/paid/council_grade endpoint returns the same rule-based grade. Informational only, not financial advice.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>",
      "side": "<side>",
      "entry_price": "<entry_price>"
    },
    "name": "trade_postmortem"
  }
}

Input Schema

Name	Required	Description
`lots`	No	Position size in lots (optional)
`side`	Yes	Trade direction: buy/long or sell/short
`symbol`	Yes	Instrument symbol, e.g. JP225, NAS100, US30, SPX500, DE30, XAUUSD, EURUSD, GBPUSD, USDJPY, AUS200
`bars_held`	No	Number of bars held (optional)
`stop_loss`	No	Initial stop-loss price (optional)
`exit_price`	No	Realized exit fill price (optional)
`mae_points`	No	Maximum Adverse Excursion in points (optional)
`mfe_points`	No	Maximum Favorable Excursion in points (optional)
`entry_price`	Yes	Entry fill price
`take_profit`	No	Initial take-profit price (optional)
`account_size`	No	Account equity in USD for risk-% check (optional)
`minutes_held`	No	Duration held in minutes (optional)
`stop_distance`	No	Initial stop distance in price points (optional alternative to stop_loss)
`entry_utc_hour`	No	UTC hour of entry 0-23 (optional; checks rollover/reset toxicity)
`low_water_price`	No	Lowest price reached while trade was open (optional)
`high_water_price`	No	Highest price reached while trade was open (optional)

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 24, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the description adds useful non-annotation context: fixed rules with no AI model calls, informational-only/not-financial-advice disclaimer, and the free-vs-paid relationship to council_grade. It stops short of describing the returned grade structure or how missing optional inputs affect scoring.

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

Conciseness4/5

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

Front-loads the key fact ('FREE rule-based trade grade') and packs the enumeration of scored dimensions densely; nearly every clause carries information. It is long but not padded, though the run-on structure could be broken up for easier scanning.

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

Completeness3/5

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

Covers what is graded and the rule basis well, but with 16 parameters, no output schema, and interdependent optional fields (stop_loss vs stop_distance, mae/mfe vs water prices), the agent is left guessing how partial inputs affect the result and what the grade actually returns.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning by tying specific rules to parameters: rollover window (entry_utc_hour), time-stop (minutes_held/bars_held), and stop floor (stop_loss/stop_distance). This tells the agent why those optional fields matter beyond their schema definitions.

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?

States a specific verb+resource (grade a trade postmortem) and enumerates what it scores: entry/exit efficiency, stop-loss placement vs ATR/MFE/MAE benchmarks, R-multiple capture, and rule compliance. It explicitly distinguishes itself from the sibling council_grade by noting that endpoint returns the same rule-based grade (paid version).

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?

Makes clear this is the FREE, fixed-rule, no-AI-model option and that the paid council_grade returns the same grade, giving the agent a clear context for choosing between them. It does not state explicit exclusion conditions (e.g., when council_grade is actually preferred), so it stays just below a 5.

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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