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alphalabs_evaluate_signal

Score YOUR trade idea through the live AlphaLabs deterministic engine: composite score, tier, per-component sub-signals, floors. Price/volume confirmation is not evaluated (no vendor market data). Returns an evaluation_id for alphalabs_explain_decision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
biasYes
thesisNoWhy it should move the stock
tickerYesSymbol, e.g. NVDA
catalystNoWhat just happened (headline/event)
confidenceNoYour own conviction 0-1 (echoed, not scored)
catalyst_typeNoOptional label, e.g. 'Government Contract'
catalyst_scoreNoOptional 0-100 materiality if you scored it

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 full burden. It discloses the tool is deterministic, does not evaluate price/volume confirmation, and returns specific outputs. It does not mention authentication, rate limits, or if the action is destructive, but for a scoring 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?

Two sentences, no waste. First sentence states purpose and outputs, second sentence adds a key limitation and links to a sibling. Information is front-loaded and each sentence earns its place.

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 7 parameters and no output schema, the description provides a good overview of functionality, outputs, and a limitation. It references a sibling tool for deeper explanation, which helps the agent. Could be improved by briefly listing expected outputs, but sufficient for basic understanding.

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 high (86%), so baseline is 3. The description adds no parameter-specific meaning beyond the schema; it mentions outputs (composite score, evaluation_id) but not how parameters map to those. The schema itself provides decent descriptions for most parameters.

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 the tool scores a trade idea and returns a composite score, tier, sub-signals, and floors. It mentions the limitation regarding price/volume confirmation and links to a sibling tool for explanation, providing a distinct purpose.

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 indicates that price/volume confirmation is not evaluated, implying when not to rely on this tool for that aspect. It also mentions returning an evaluation_id for alphalabs_explain_decision, suggesting a workflow. However, it does not explicitly compare with siblings like alphalabs_calibration_report or alphalabs_outcome_report.

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

A4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: calibration telemetry, signal evaluation, explanation, feature attribution, catalog listing, and outcome reporting. No two tools overlap in functionality.

Naming Consistency4/5

All tools share the 'alphalabs_' prefix and follow a verb_noun or noun_noun pattern. The mix of verb_noun (evaluate_signal, explain_decision, get_catalog) and noun_noun (calibration_report, feature_attribution, outcome_report) is a minor inconsistency, but still predictable and readable.

Tool Count5/5

With 6 tools, the server is neither too sparse nor too heavy. Each tool covers a distinct aspect of the intelligence pipeline, making the set well-scoped for its purpose.

Completeness5/5

The tool surface covers the full lifecycle of signal evaluation: evaluation, explanation, feature analysis, calibration reporting, and outcome tracking. Missing features like market data access are explicitly out of scope, so no dead ends remain.

Resources