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Glama

patternfetch

by io.github.MarvinRey7879

Server Capabilities

patternfetch_capabilities
Read-onlyIdempotent

Return patternfetch's own capability matrix: which asset classes are covered (US stocks, ETFs, crypto spot), the data source and delay for each, the supported timeframes, the endpoint list, the per-call prices and tier limits, and the product version. Takes no arguments and returns the same static self-description on every call — it contains NO market data (no quotes, candles, patterns or base rates). WHEN: once at the start of a session, to learn which asset classes and timeframes are supported before calling brief/multi/delta/analogs/scan, instead of guessing and getting a validation error. WHEN NOT: you already know the ticker and timeframe are supported (go straight to brief), or you want actual market data (this returns none).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already signal read-only, idempotent, non-destructive behavior. The description adds significant context: it takes no arguments, returns static self-description on every call, and explicitly states it contains no market data. There is no contradiction with annotations.

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 well-structured with a clear listing of what is returned and separate WHEN/WHEN NOT sections. It is slightly verbose but every sentence adds value, and critical information is front-loaded.

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?

For a zero-parameter tool with no output schema and rich annotations, the description is fully complete. It explains the return content, use cases, and what the tool does not provide, ensuring the agent can use it correctly without additional context.

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

Parameters4/5

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

There are zero parameters, so the baseline is 4. The description confirms 'Takes no arguments', which is sufficient. No additional parameter details are needed.

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 uses a specific verb 'Return' and clearly defines the resource 'patternfetch's own capability matrix'. It enumerates exactly what the matrix contains (asset classes, data sources, timeframes, etc.) and explicitly distinguishes it from sibling tools by stating it contains NO market data.

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?

The description provides explicit WHEN and WHEN NOT sections: use it at session start to learn supported features, and avoid it if you already know the ticker/timeframe support or need actual market data. This effectively guides the agent on when to call this tool versus its siblings.

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