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list_indicators

List indicators, or fetch one indicator's full schema.

    Cheap, cacheable per session.

    With no arguments: a compact catalog — ``{"indicators": [...],
    "count": N}`` — where each entry carries id, name, category, kind,
    and value_dtype (no description, to keep the discovery scan small). Use
    it to discover what exists. Pass name='rsi' (id or name,
    case-insensitive) to get that single indicator's complete entry
    including its description and params_schema — do this before adding an
    indicator to a strategy so its parameters are exactly right.
    Pass compact=False for full entries for everything (large; the MCP
    server may cap it and set ``truncated_by_mcp`` — prefer compact or
    name=).

    Wire optimization: the compact discovery path asks the engine to omit
    per-entry descriptions (``descriptions=false``) since they are stripped
    locally anyway; the name= and compact=False paths request them. This is
    a pure saving — if the engine ignores the param it returns full entries
    and the local compact strip still yields a lean result.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
compactNo

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: cheap and cacheable per session, return format differences between compact and full modes, truncation behavior indicated by 'truncated_by_mcp', and wire optimization that requests descriptions conditionally. This gives the agent a clear understanding of side effects and internal behavior.

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 detailed and well-structured with clear sections, code blocks for return shapes, and front-loaded purpose. However, it is somewhat verbose, with the wire optimization paragraph being somewhat technical and possibly unnecessary for most uses. Every sentence does add value, but a slightly shorter version would be ideal.

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 tool with two parameters and no output schema, the description covers all relevant aspects: what is returned in each mode, how to use arguments, edge cases like truncation and case-insensitivity, and best practices. It leaves no ambiguity about the tool's behavior and is complete for an AI agent to select and invoke correctly.

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?

The schema coverage is 0%, so the description carries the full burden. It explains that 'name' accepts an id or name (case-insensitive) and causes full entries, while 'compact' controls whether entries are compact or full, with default true. It also explains the interaction between name and compact, adding significant meaning beyond the 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?

The description clearly states two primary use cases: listing all indicators as a compact catalog, or fetching a single indicator's full schema by name. It distinguishes between these modes and implies that this tool is for indicator discovery, differentiating from sibling tools like get_catalog or get_strategy_schema.

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 guidance on when to use each argument combination: no arguments for discovering available indicators, name='rsi' before adding an indicator to a strategy, and compact=False for full entries but warns about potential truncation. It also suggests preferring compact or name= over compact=False to avoid truncation, and explains wire optimization as a behind-the-scenes saving.

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

A4.2/5.0
Disambiguation5/5

Every tool targets a distinct operation or resource: backtesting, comparison, macro data, reference catalogs, etc. Even similar tools like run_backtest and compare_backtests are clearly differentiated by purpose and inputs.

Naming Consistency4/5

Overall consistent verb_noun pattern in snake_case, with a few exceptions like engine_info (noun_noun) and export_backtest (verb_noun but less common verb). The pattern is predictable and aids agent selection.

Tool Count4/5

20 tools is slightly above the ideal range but justified by the breadth of the platform (backtesting, data retrieval, reference, export). Each tool serves a clear purpose without redundancy.

Completeness4/5

Covers the full backtesting lifecycle: strategy validation, data sourcing, backtesting, comparison, export, and reference lookups. Minor gaps exist (e.g., no explicit strategy persistence), but the core workflow is complete.

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