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luxalgo-mcp-server

by io.github.LuxAlgo

Get a Library indicator

library_get_indicator

Details for one indicator: what it does, how to read it, family, concept links, preview image — plus whether its source code is available (fetch the code itself with library_get_source_code). Use when the user asks about a specific indicator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesIndicator slug, e.g. 'tri-star'
contextYesExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."

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 behavioral disclosure burden. It discloses what the tool returns (details, family, concept links, preview image) and clarifies a key boundary: it only indicates source-code availability and does not return the code, directing to a sibling for that. This gives the agent accurate expectations beyond the input schema.

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?

Two sentences accomplish a lot: the first enumerates the return content compactly, the second gives the usage trigger and a cross-reference to the source-code sibling. Every phrase adds value with no repetition 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?

Given the tool's simple scope and lack of an output schema, the description adequately covers what the agent needs: what the tool does, what information it returns, when to use it, and what it deliberately does not return. The schema covers parameters fully, so no critical information is missing.

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%, so the baseline is 3. The schema fully documents 'slug' and the 'context' parameter with examples and constraints. The description does not need to add parameter details, and it doesn't, which is acceptable.

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 and resource ('Details for one indicator') and enumerates exact content: what it does, how to read it, family, concept links, preview image, and source-code availability. This clearly differentiates it from list/search/source-code siblings by focusing on a single indicator's detailed profile.

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?

Explicitly states when to use: 'Use when the user asks about a specific indicator.' It also points to library_get_source_code as the alternative for fetching code itself. It does not explicitly contrast with library_search or library_list_indicators for non-specific queries, but the guidance is clear enough for correct tool selection.

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

A4.1/5.0
Disambiguation4/5

The domain prefixes (edge_, library_, propfirms_, trackers_) cleanly separate four distinct areas, and within most clusters each tool has a specific job (search vs get vs list vs simulate). The propfirms cluster is the only strain: propfirms_simulate, propfirms_simulate_trades, propfirms_pass_rates, propfirms_compare, and propfirms_validate_strategy all overlap in the broad sense of 'running simulations,' though the descriptions do differentiate them by input type and scope.

Naming Consistency4/5

Each domain follows its own consistent pattern: library_get_*/library_list_*, propfirms_* with an action verb, edge_* and trackers_* as noun-style resources. The convention is recognizable and predictable per domain, with only minor deviations like propfirms_challenge_rules and propfirms_pass_rates being noun-first rather than verb-first.

Tool Count3/5

At 28 tools the server is heavy, but the count is justified by four large, distinct product surfaces (Library, Edge Stats, prop-firm simulation, and market trackers). Each individual cluster is reasonably scoped; the propfirms cluster alone accounts for 12 tools, which pushes the total into the 'too many' range even though the breadth is real.

Completeness5/5

Each domain feels complete: edge stats has catalog, symbol discovery, and report retrieval; the Library has search, browse, get, and source-code access; propfirms has search, get, rule encoding, simulation, trade-series simulation, comparison, optimization, and validation; trackers has dataset discovery, query, latest-publish, and cross-dataset ticker dossiers. There are no obvious dead ends or missing lifecycle steps within the read-only/analysis scope the server targets.

Resources