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luxalgo-mcp-server

Search the LuxAlgo Library

library_search

Search the LuxAlgo Library — the encyclopedia of trading and technical analysis. One query over 800+ concepts (alias-aware: 'stochastics' finds Stochastic Oscillator) and 800+ ready-to-use indicators. Start here whenever you have a name, informal term, or topic; results carry slugs for the get tools plus canonical URLs for citation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoLimit to 'concepts' (explanations) or 'indicators' (implementations); default all
limitNoMax results (default 10)
queryYesA concept, indicator name, alias, or topic — e.g. 'order blocks', 'mean reversion', 'stochastics'
familyNoNarrow to one concept family
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

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals useful traits: alias-aware search ('stochastics' finds Stochastic Oscillator), coverage breadth (800+ concepts and indicators), and result contents (slugs and canonical URLs). It does not cover failure behavior or read-only guarantees, but for a search tool this is reasonable.

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?

Three sentences, each earning its place: the first names the tool and resource, the second details capability with a concrete example, the third gives usage guidance and output content. Information is front-loaded and there is zero filler.

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 five parameters and no output schema, the description provides enough operational context: what is searched, when to use it, what results contain, and how results connect to sibling tools. The unusual context parameter is fully documented in the schema, so it need not be repeated here.

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 description adds some nuance beyond the schema by framing query as an 'informal term or topic' and giving an alias example, but it does not add meaning to the type, limit, family, or context parameters that the schema does not already provide.

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 states a specific verb ('Search'), a specific resource ('the LuxAlgo Library'), and the scope ('800+ concepts' and '800+ ready-to-use indicators'). It also distinguishes the tool from its get/list siblings by noting that results 'carry slugs for the get tools' and by branding it as the entry point ('Start here').

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?

It explicitly tells the agent when to start: 'Start here whenever you have a name, informal term, or topic.' It also implies the downstream workflow by saying results carry slugs for the get tools. However, it does not explicitly say 'if you already have a slug, use library_get_* instead,' so the when-not guidance is slightly implicit.

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.