AlphaLabs Intelligence

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ticker": "<ticker>",
      "bias": "<bias>"
    },
    "name": "alphalabs_evaluate_signal"
  }
}

Input Schema

Name	Required	Description
`bias`	Yes
`thesis`	No	Why it should move the stock
`ticker`	Yes	Symbol, e.g. NVDA
`catalyst`	No	What just happened (headline/event)
`confidence`	No	Your own conviction 0-1 (echoed, not scored)
`catalyst_type`	No	Optional label, e.g. 'Government Contract'
`catalyst_score`	No	Optional 0-100 materiality if you scored it

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 19, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses it is a live deterministic engine and lacks price/volume confirmation. However, it does not mention idempotency, rate limits, side effects, or authentication requirements, leaving gaps for a scoring tool.

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 concise sentences: output overview, limitation, and return value. Front-loaded with key purpose, no fluff. Every sentence is informative.

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?

The description explains the key outputs (composite score, tier, sub-signals, floors) and the evaluation_id for chaining. It lacks details on whether the tool stores results or side effects, but given no output schema, it covers essentials.

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 does not add meaning beyond the schema's parameter descriptions; it only mentions output components without linking to inputs.

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, produces a composite score, tier, sub-signals, and floors. It explicitly mentions returning an evaluation_id for use with the sibling tool alphalabs_explain_decision, distinguishing its role.

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 explains the tool evaluates trade ideas, notes that price/volume confirmation is not evaluated, and indicates the returned evaluation_id is for use with alphalabs_explain_decision. It provides clear context but does not explicitly state when not to use or compare to other siblings.

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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