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demeet2k

Athena MCP Server

by demeet2k

athena_gp_decision_evsi

Estimate the downstream decision value of candidate Gaussian process measurements using conditional-Gaussian Monte Carlo simulation to guide experiment design.

Instructions

Estimate downstream decision value of candidate GP measurements by conditional-Gaussian Monte Carlo. DESIGN_ONLY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
actionsYes
samplesNo
context_keyYes
cost_weightNo
experimentsYes
risk_weightNo
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'DESIGN_ONLY' as a mode constraint but does not state whether the tool is read-only, has side effects, requires specific permissions, or what the output structure is. The method is mentioned but not the resulting behavior or side effects.

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 extremely concise, with two short sentences that each serve a purpose: the first states the core function, the second provides a design-only constraint. No redundant or filler words; it is front-loaded and easily scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 7 parameters, no output schema, and no annotations, the description is severely inadequate. It does not explain the expected output, the meaning of the parameters, or how to handle edge cases. It is far from complete for an agent to invoke correctly without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description provides no explanation of the required parameters (context_key, actions, experiments) or optional ones (seed, samples, cost_weight, risk_weight). The phrase 'candidate GP measurements' vaguely relates to experiments but does not add meaningful semantics. The description completely fails to compensate for the lack of schema documentation.

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's function: 'Estimate downstream decision value of candidate GP measurements' with a specific method ('conditional-Gaussian Monte Carlo'). It distinguishes from sibling tools like athena_decision_evsi (generic EVSI) and athena_gp_bma_decision_evsi (BMA variant) by explicitly focusing on GP measurements.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. The 'DESIGN_ONLY' phrase implies a design-phase constraint but does not explain when to choose this over other EVSI/GP decision tools. There are no exclusions, prerequisites, or alternative suggestions.

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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