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list_indicators

Discover all FLOX-supported indicators with their signatures, batch functions, and output shapes. Use it to find available indicators or verify an indicator name before suggesting one.

Instructions

List every indicator exposed by the FLOX Python binding with its class signature, batch function (if any), and shape (single-input scalar, OHLC tuple, multi-output, …). Use this when the user asks 'what indicators does FLOX support' or before suggesting an indicator name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoOptional substring filter; case-insensitive.

Schema Changelog

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

  1. Addedv0.6.9
  2. Removedv0.6.8
  3. First observedv0.6.3

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses what is returned (class signature, batch function, shape), implying a read-only listing operation. It does not explicitly state side-effect freedom or performance characteristics, but the simple list nature makes these omissions minor.

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?

The description is two sentences: the first states the tool's purpose and output contents, the second provides usage context. Every word earns its place, with no redundancy or filler.

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 simple list tool with one optional parameter, the description adequately covers the expected output and use context. The output schema is absent, but the description enumerates the key fields (class signature, batch function, shape), making the tool's behavior sufficiently clear 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 description coverage is 100% for the single 'filter' parameter, so the baseline is 3. The description adds no further meaning to the parameter; it only says 'case-insensitive' in the schema, which is sufficient. No extra semantic value beyond the schema is provided.

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 ('List every indicator') and resource ('FLOX Python binding') and clearly distinguishes the tool's scope by detailing the returned fields (class signature, batch function, shape). It also positions itself relative to user intent, separating it from sibling tools that focus on specific indicators or other functions.

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 explicitly tells when to use it: 'when the user asks what indicators does FLOX support' or 'before suggesting an indicator name.' This gives clear context but does not name alternatives or exclusions, so it stops short of the full 'when/when-not/alternatives' guidance.

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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