fxabsolute-mcp

fxa_session_scan

Extract and summarize specific bars from any trading session across a date range, with daylight saving handled. Get session-specific candle data without filtering raw history yourself.

Instructions

Pull specific bars out of a trading session across a whole date range, and summarise them. Example: the first 5-minute candle of the New York session every day for six months (instrument SPX500, session nyse, timeframe M5, bars [1]). Session windows are evaluated in exchange-local time with daylight saving handled, so results do not shift by an hour in summer. Use this instead of pulling raw candles and filtering them yourself.

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": {
      "instrument": "<instrument>"
    },
    "name": "fxa_session_scan"
  }
}

Input Schema

Name	Required	Description
`to`	No	ISO date YYYY-MM-DD
`bars`	No	Which bars of the session to take, 1-based (1 = the opening bar). Use ["all"] for every bar in the session. Default [1].
`from`	No	ISO date YYYY-MM-DD
`session`	No	One of: sydney, tokyo, london, newyork, nyse, overlap. "nyse" is the 09:30–16:00 New York cash session; "newyork" is the wider 08:00–17:00 FX session.
`timeframe`	No	Default M5.
`daysOfWeek`	No	Optional filter, e.g. ["Monday","Friday"] or [1,5].
`instrument`	Yes
`includeRows`	No	How many individual rows to return alongside the summary. Default 50.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 12, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description adds valuable behavioral context: session windows are evaluated in exchange-local time and daylight saving is handled, preventing hour shifts. However, it does not disclose return format or summary contents, which would be important for a summarizing tool, but the disclosed behavior is meaningful.

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 (three sentences) and well-structured: action, example, DST behavior, then alternative usage. Each sentence earns its place with no redundancy.

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?

No output schema exists, and the description does not explain what the summary consists of or how includeRows affects output. This is a notable gap for a tool whose core purpose is summarization, though the example and parameter descriptions provide enough context for basic 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?

Schema coverage is 88%, but the description enhances parameter understanding with a concrete example that clarifies relationships: 'bars [1]' means the opening bar, session 'nyse' is the cash session, and timeframe 'M5' is specified. This adds meaning beyond the schema's field descriptions.

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 verb 'Pull specific bars out of a trading session' and specifies the resource (trading session data) with a summarization output. It distinguishes from sibling fxa_candles by emphasizing summarization and pre-filtered session bars.

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 'Use this instead of pulling raw candles and filtering them yourself,' naming the alternative and giving clear guidance on when to prefer this tool. The example and DST/Exchange-local-time note further define appropriate usage conditions.

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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