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Glama

patternfetch

by io.github.MarvinRey7879

Market-State Brief

patternfetch_brief
Read-onlyIdempotent

Get a token-compact market-state brief for a stock, ETF, or crypto ticker + timeframe. Returns compact candles, detected chart/candlestick patterns with geometric confidence AND a backtested historical base rate (how often that pattern+timeframe+confidence-band actually resolved its way), support/resistance levels, trend/regime, and interpreted indicators (RSI/EMA state) plus a one-line summary. Covers US stocks/ETFs (split & dividend adjusted, delayed/EOD) and crypto spot (realtime). WHEN: an agent needs the current technical picture of a market without dumping raw OHLCV into context (saves tokens, avoids numeric hallucination). WHEN NOT: you need order execution or portfolio advice. Examples: {"ticker":"AAPL","timeframe":"1d"}, {"ticker":"BTC/USDT","timeframe":"4h"}. Output is impersonal market data, NOT investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many recent bars to analyse: integer >= 20, capped at 1000, default 200. Fewer bars = fewer tokens.
marketNoOptional asset-class override. Omit and it is inferred from the ticker: a "BASE/QUOTE" pair is crypto, a plain symbol is a US stock/ETF.
tickerYesSymbol to analyse. US stock or ETF like "AAPL" / "SPY", or a crypto spot pair like "BTC/USDT". Always spell crypto as a pair: a bare "BTC" or "ETH" is a real US-listed ETF, NOT the coin, and it will return that ETF's prices without failing. Affected responses carry a "notice" field.
timeframeYesBar size. One of 1m, 5m, 15m, 30m, 1h, 4h, 1d, 1w. Coverage differs per asset class — see the capabilities tool.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / ticker / description
      Previous value: -"Symbol to analyse. US stock or ETF like \"AAPL\" / \"SPY\", or a crypto spot pair like \"BTC/USDT\"."New value: +"Symbol to analyse. US stock or ETF like \"AAPL\" / \"SPY\", or a crypto spot pair like \"BTC/USDT\". Always spell crypto as a pair: a bare \"BTC\" or \"ETH\" is a real US-listed ETF, NOT the coin, and it will return that ETF's prices without failing. Affected responses carry a \"notice\" field."
  2. Changed4 schema fields changed
    • addedInput schema / properties / limit / description
      Added value: +"How many recent bars to analyse: integer >= 20, capped at 1000, default 200. Fewer bars = fewer tokens."
    • addedInput schema / properties / market / description
      Added value: +"Optional asset-class override. Omit and it is inferred from the ticker: a \"BASE/QUOTE\" pair is crypto, a plain symbol is a US stock/ETF."
    • addedInput schema / properties / ticker / description
      Added value: +"Symbol to analyse. US stock or ETF like \"AAPL\" / \"SPY\", or a crypto spot pair like \"BTC/USDT\"."
    • addedInput schema / properties / timeframe / description
      Added value: +"Bar size. One of 1m, 5m, 15m, 30m, 1h, 4h, 1d, 1w. Coverage differs per asset class — see the capabilities tool."
  3. Changed1 schema field changed
    • addedInput schema / properties / market
      Added value: +{
+  "enum": [
+    "crypto",
+    "stock"
+  ],
+  "type": "string"
+}
  4. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds crucial context: coverage of US stocks/ETFs (split/dividend adjusted, delayed/EOD) and crypto spot (realtime), plus ticker ambiguity warning. No contradictions.

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?

Description is well-structured with clear sections and examples, but slightly lengthy. Every sentence provides value; formatting improves readability.

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?

Despite no output schema, the description thoroughly explains what the tool returns (patterns with confidence, backtested base rates, support/resistance, trend/regime, indicators, one-line summary). It covers asset class differences and token-saving rationale, making it complete for an AI agent.

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 coverage is 100%, and the description adds significant meaning beyond the schema: for ticker (crypto pair vs stock ETF, notice field), timeframe (coverage differs per asset class, see capabilities tool), market (optional override), limit (fewer bars = fewer tokens).

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 purpose: 'Get a token-compact market-state brief for a stock, ETF, or crypto ticker + timeframe.' It lists specific outputs (candles, patterns, support/resistance, etc.) and distinguishes from siblings by focusing on a single ticker with compact summary.

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?

Explicit 'WHEN' and 'WHEN NOT' conditions are provided: use when needing technical picture without raw data, avoid for order execution or portfolio advice. Examples for ticker and timeframe are given, aiding correct invocation.

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

All patternfetch toolsInspect
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TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: analogs for historical patterns, brief for current state, capabilities for metadata, delta for changes, multi for multi-timeframe, scan for screening. No overlap in functionality.

Naming Consistency5/5

All tools follow a consistent 'patternfetch_<noun>' pattern using snake_case, making it predictable. No mixed conventions or ambiguous verbs.

Tool Count5/5

6 tools is well-scoped for a technical analysis server. Each tool earns its place, covering key operations without being excessive or insufficient.

Completeness4/5

The tool set covers core workflows: current state, multi-timeframe, scanning, historical analogs, and polling for changes. Minor gaps (e.g., no raw data endpoint) are acceptable given the server's focus on processed insights.

Resources