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chrbailey

promptspeak-mcp-server

ps_symbol_add_alternative

Add alternative explanations to pattern-based findings to prevent false positives by documenting multiple possible interpretations and adjusting confidence levels.

Instructions

Add an alternative explanation to a symbol's findings.

CRITICAL for preventing false positives: When a pattern-based finding could have multiple explanations, document them here. Adding high-likelihood alternatives automatically reduces confidence in the original claim.

Examples:

  • "9 identical payments" → Alternative: "Monthly insurance premium financing"

  • "Large round numbers" → Alternative: "Negotiated contract amounts"

  • "Vendor with single customer" → Alternative: "Subsidiary company"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolIdYesSymbol ID to update
alternativeYesDescription of the alternative explanation
likelihoodYesEstimated likelihood this alternative is correct (0-1)
reasoningNoWhy this alternative is plausible
added_byYesWho is adding this alternative
Behavior4/5

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

With no annotations provided, the description carries full burden. It clearly describes the tool's effect ('automatically reduces confidence in the original claim') and provides important context about its purpose in preventing false positives. However, it doesn't mention potential side effects, permissions needed, or error conditions.

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?

The description is efficiently structured with a clear purpose statement, critical usage context, and concrete examples. Every sentence serves a distinct purpose: establishing the action, explaining why it matters, and illustrating proper use. No wasted words.

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?

For a mutation tool with no annotations and no output schema, the description does well by explaining the tool's purpose, when to use it, and its behavioral effect. The examples provide valuable context. However, it doesn't describe what happens on success/failure or the return format.

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 all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, but the examples help illustrate how parameters like 'alternative' and 'reasoning' might be used in practice.

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 explicitly states the verb ('Add') and resource ('alternative explanation to a symbol's findings'), making the purpose clear. It distinguishes from siblings like ps_symbol_create or ps_symbol_update by focusing specifically on adding alternative explanations rather than general symbol operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'CRITICAL for preventing false positives: When a pattern-based finding could have multiple explanations, document them here.' It also explains the effect ('automatically reduces confidence in the original claim') and gives concrete examples of appropriate use cases.

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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