tickscope-mcp

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detect_cross

Detect crossover and crossunder events between two indicators or price series. Returns the latest values, current relation, whether a cross occurred on the last bar, and bars since the last cross.

Instructions

Pine-style crossover analysis (ta.crossover/ta.crossunder).

series_a and series_b are each an indicator spec (ema:20, ta.sma(50)), a price source (close, hl2, …) or a numeric constant ("30"). Example: a golden cross is series_a="ema:50", series_b="ema:200". Returns the latest values, their relation, whether a cross happened on the last bar, and how many bars since the last cross.

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>",
      "series_a": "<series_a>",
      "series_b": "<series_b>"
    },
    "name": "detect_cross"
  }
}

Input Schema

Name	Required	Default
`limit`	No
`symbol`	Yes
`exchange`	No
`series_a`	Yes
`series_b`	Yes
`timeframe`	No	1h

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Aug 6, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains the input format handling and the output elements (latest values, relation, cross occurrence, bars since last cross), but omits explicit read-only confirmation and failure or edge-case behavior.

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 tightly written, with every sentence contributing value. It leads with the core function, explains input syntax, gives an explicit example, and summarizes the return payload without redundancy.

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

Completeness4/5

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

The description adequately covers the tool's purpose, key parameters, and return summary for a moderately complex technical analysis tool. Minor gaps remain in explaining limit semantics and how to integrate other parameters, but the overall context is sufficient.

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?

Given 0% schema description coverage, the description must compensate. It thoroughly explains series_a and series_b with formats ('ema:20') and an example, but does not describe parameters like limit, timeframe, symbol, or exchange, leaving them to inference from titles/defaults.

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 'Pine-style crossover analysis' with reference to ta.crossover/ta.crossunder, defining the tool's exact function. It distinguishes from sibling detection tools by specifying crossover detection, and provides a concrete golden cross example.

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?

The description provides clear context on when to use the tool (for crossover/crossunder analysis) and gives an example use case. It does not explicitly list exclusion criteria or alternative tools, but the intended usage is evident from the explanation.

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