fxabsolute-mcp

fxa_level_touches

Test whether a price level actually held by finding reaches, de-duplicating clustered touches, and measuring subsequent reactions.

Instructions

Test whether a price level actually held. Finds every time price reached the level, de-duplicates clustered touches, and measures the reaction that followed. This is the numeric version of asking whether a horizontal line on the chart is respected.

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>",
      "price": "<price>"
    },
    "name": "fxa_level_touches"
  }
}

Input Schema

Name	Required	Description
`to`	No
`from`	No
`price`	Yes	The level to test.
`timeframe`	No	Default M15.
`instrument`	Yes
`includeRows`	No	Default 40.
`tolerancePips`	No	How close counts as a touch. Default 5.
`separationBars`	No	Minimum bars between distinct touches. Default 4.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 12, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations exist, so the description carries the full burden. It discloses the algorithm: finds touches, de-duplicates cluster, and measures reaction. This goes beyond a simple 'tests levels' and provides meaningful behavioral detail, though it omits output format specifics.

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?

Three concise sentences. The first sentence front-loads the core purpose, the second adds process detail, and the third uses a useful analogy. No unnecessary words.

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?

Given 8 parameters and no output schema, the description explains the concept well but lacks explicit return-value specification. 'Measures the reaction' is vague; the agent doesn't know what numeric output to expect. Time range parameters (from/to) are not addressed, leaving some completeness gaps.

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 63%, and the description adds context for key parameters: 'de-duplicates clustered touches' explains separationBars, and 'how close counts' relates to tolerancePips. It doesn't explain to/from but the schema already has no descriptions for those, so the description partially compensates.

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's function: 'Test whether a price level actually held' with a specific verb and resource. It also explains the process (finds touches, de-duplicates, measures reaction), making it distinct from siblings like candle retrieval or session scanning.

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 ('when you want to test a price level') but no explicit alternatives or when-not scenarios are mentioned. The description doesn't contrast with sibling tools, so the agent must infer applicability from the purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server