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luxalgo-mcp-server

by io.github.LuxAlgo

Get a hosted Edge Stats report

edge_report

One precomputed session-statistics result: P(outcome | conditions) for a preset on a hosted symbol, in the engine's full honesty envelope — the estimate with N and a Wilson 95% confidence interval, minimum-sample guards, a first-half vs second-half stability split, per-year counts, the value distribution where the outcome is continuous, and the disclaimer. Historical conditional frequencies, not predictions. Preset ids come from edge_presets; symbols from edge_symbols.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
presetYesPreset id, e.g. 'gap-fill' — see edge_presets
symbolYesHosted symbol, e.g. 'BTCUSDT' — see edge_symbols
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

A4.4/5.0
Behavior5/5

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

With no annotations, the description fully carries behavioral disclosure. It reveals that the tool returns a precomputed read-only estimate with a specific honesty envelope: Wilson 95% CI, N, minimum-sample guards, stability split, per-year counts, value distribution, and disclaimer, plus the caveat that these are historical frequencies, not predictions.

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 dense but free of filler, front-loading the core result and then enumerating the report contents. The long first sentence packs many useful specifications; it could be slightly more scannable as a list, but every element earns its place.

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?

In the absence of an output schema, the description enumerates all meaningful return components and the source of valid preset/symbol ids. Combined with the historical-not-predictive caveat, the agent has enough information to invoke the tool correctly.

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 schema already documents preset, symbol, and context. The description adds the relationship between these parameters and the output probability, but it does not need to compensate for missing schema documentation; baseline 3 is appropriate.

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?

Description states a specific resource ('hosted Edge Stats report') and the exact semantic content: a precomputed P(outcome | conditions) result for a preset on a hosted symbol. It clearly differentiates from siblings by defining the report as a single statistical artifact and pointing to edge_presets/edge_symbols as source lists.

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 gives clear context: use when you need a precomputed conditional-frequency estimate for a known preset/symbol, and it warns that the result is not a prediction. It does not explicitly enumerate when-not-to-use alternatives, but the dependency chain and historical-frequency caveat make the intended usage clear.

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