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Glama

patternfetch

by io.github.MarvinRey7879

What Changed Since Last Brief

patternfetch_delta
Read-only

Get only what CHANGED since your last brief for a ticker+timeframe (trend flips, new patterns, RSI-state changes). WHEN: an agent polls the same market repeatedly and wants minimal tokens — call brief once, then delta on each later poll. WHEN NOT: first look at a market (use brief). Returns changed=false when nothing material changed. Example: {"ticker":"BTC/USDT","timeframe":"4h"}. Impersonal data, not 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

A4.6/5.0
Behavior5/5

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

Description adds context beyond annotations: it specifies that the tool returns changed=false when nothing material changed, and states 'Impersonal data, not advice,' reinforcing the read-only nature. This aligns with readOnlyHint=true and no destructive side effects.

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?

Description is extremely concise: one sentence for purpose, then WHEN/WHEN NOT, return value note, example, and final disclaimer. Every sentence earns its place, well-structured and front-loaded.

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?

Given the tool has 4 parameters and no output schema, the description covers the key behavioral aspects: what changes are detected, return value indication, and usage context. Could be slightly more detailed on output structure, but sufficient for an agent.

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%, so schema already documents parameters. Description adds minor value (e.g., 'Fewer bars = fewer tokens' for limit, example for ticker/timeframe) but does not significantly enhance understanding beyond schema.

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?

Description clearly states the tool gets only what changed since the last brief for a ticker+timeframe, listing specific change types (trend flips, new patterns, RSI-state changes). It explicitly distinguishes from sibling patternfetch_brief by stating when to use each.

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?

Provides explicit WHEN (agent polls same market repeatedly and wants minimal tokens) and WHEN NOT (first look at a market, use brief) conditions. Includes an example JSON and notes that return value indicates if anything changed.

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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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