FILTRIX.net

get_ticker_gap_stats

Read-onlyIdempotent

How ONE ticker's own past gap days resolved: number of gaps, and for the gap day and the next day the high/low spike, range, open-to-close return, change vs prior close, time of high/low and how often it closed red (definitions included). gap_threshold is in percent (10 = 10%). Free tier: 3 distinct tickers a day. For gappers in general use run_backtest.

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>"
    },
    "name": "get_ticker_gap_stats"
  }
}

Input Schema

Name	Required	Description	Default
`stat`	No	How to aggregate the per-gap figures: avg (mean) or median.	avg
`symbol`	Yes	Ticker symbol, e.g. AAPL (case does not matter).
`direction`	No	up for gap-ups, down for gap-downs.	up
`gap_threshold`	No	Minimum gap size in percent (10 = 10%).

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 9, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds the free-tier quota, which is genuine new behavioral information, but says nothing about data history depth, latency, or what happens when a ticker has no qualifying gaps.

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-loaded with what the tool computes and ends with the scope constraint and alternative. The long enumeration of return metrics partly duplicates the existing output schema, which is the main bit of unearned length.

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?

An output schema exists, so the return-value list needn't be in the description, and all four parameters are schema-documented. The description covers the quota and the sibling boundary; only the no-gaps/empty-result behavior is left unstated.

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?

Schema coverage is 100% and each parameter carries its own description, including the enum meanings for stat and direction. The description only repeats the gap_threshold percent convention (10 = 10%) and adds nothing for stat or direction, so it sits at the baseline for fully documented schemas.

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 and resource: past gap-day statistics for ONE ticker, with the exact metrics computed (spike, range, open-to-close return, red-close frequency). The 'ONE ticker' emphasis plus the pointer to run_backtest makes the boundary with the sibling unambiguous.

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?

Explicitly routes general gap analysis to run_backtest and discloses a concrete usage constraint (free tier: 3 distinct tickers a day). It lacks an explicit when-not statement but the alternative and the rate limit give clear selection context.

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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