Skip to main content
Glama
Sign In
Pattern Vista

Pattern Vista

by xsf0221
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

recommended_patterns

Retrieve daily flagged chart patterns with their historical win rates over SPY for the next 20 trading days. Use hist_win_w20 to identify which pattern buckets show a historical edge.

Instructions

The latest K-line chart patterns the daily scan flagged, each carried with the historical win-rate of its own pattern bucket.

hist_win_w20 is the share of past occurrences of that (pattern type, direction) bucket that beat SPY over the following 20 trading days — an aggregate property of the bucket, NOT a probability for this specific signal. Anything at or below 0.5 has no historical edge over simply holding the index, and several bullish buckets sit well below it; report the number rather than treating a flagged pattern as a recommendation.

Args: limit: Max rows, up to 100. Free accounts receive 2 rows and a null hist_win_w20 — check is_billed before drawing conclusions from an empty win-rate.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "recommended_patterns"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries full responsibility. It discloses a critical non-obvious behavior: hist_win_w20 is an aggregate bucket property, not a probability for the specific signal, and that values ≤0.5 imply no edge. It also reveals free-tier behavior (2 rows, null win-rate, is_billed flag). This is far beyond typical transparency and directly prevents misinterpretation.

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?

The description is front-loaded with the core purpose, then provides essential interpretive detail in a separate paragraph, and ends with a clean Args section. It is not overly verbose given the importance of the caveats; each sentence earns its place, though the phrasing could be tightened slightly without losing meaning.

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

Completeness5/5

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 format is not needed in the description. The description covers what the tool returns (patterns with win-rates), how to interpret the win-rate, the impact of the limit on free accounts, and the logical check (is_billed). Combined with the output schema, an agent has everything needed to invoke it correctly and interpret results safely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and there is only one parameter (limit). The description adds significant meaning: it defines the effective maximum (100), the free-account default behavior (2 rows), and the side effect of null hist_win_w20. This fully compensates for the lack of schema 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 resource: K-line chart patterns flagged by the daily scan, with the win-rate of their pattern bucket. It uses a specific subject and modifies it with the key field (hist_win_w20). It is distinct from siblings like market_stretch and deviation_ranking, which focus on other metrics.

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 gives clear context for when to use the tool (for pattern results) and, more importantly, how to interpret the output responsibly (report the number, not a recommendation). It notes the free-tier limitation indirectly via the `limit` parameter and `is_billed` check. However, it does not explicitly mention alternatives like 'when NOT to use this tool', but the scope is so specific that this is not a major gap.

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

Deploy Server

Other Tools