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AntonIXO

vibescience-mcp

by AntonIXO

propose_hypothesis

Propose a causal explanation or intervention for a registered problem, committing at least one predicted effect on a diagnostic to enable calibration and verdict tracking.

Instructions

Propose an explanation/intervention for a problem. You MUST commit ≥1 predicted_effect on a REGISTERED diagnostic BEFORE testing — this is rejected otherwise. Committing the prediction up front is what turns this log into a calibration signal. Call recall first. To revise a dead end, pass supersedes rather than deleting it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNo
planNo
papersNo
rationaleNo
statementYes
problem_idYes
supersedesNoid of a prior hypothesis this one revises
topic_tagsNo
problem_tagsNo
interventionsNo
predicted_effectsYesREQUIRED ≥1: [{diagnostic_id, direction: up|down|none, magnitude_note}]. The FIRST is the primary prediction the verdict keys off.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations only provide destructive=false and idempotent=false. Description adds crucial behavioral details: requires committed predicted_effects on registered diagnostic, explains why (calibration signal), and how to revise (supersedes). No contradictions.

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 sentences with front-loaded purpose. Every sentence provides essential guidance without redundancy. 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?

Given 11 parameters, 3 required, with output schema, the description covers the core workflow well. However, many optional parameters are not described, and the low schema coverage means some details are missing. Still comprehensive for the main task.

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 very low (18%). The description adds meaning for 'predicted_effects' (required array with structure) and 'supersedes', but many parameters (id, plan, papers, etc.) remain unexplained. Partially compensates for low schema coverage.

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 'Propose an explanation/intervention for a problem' with a specific verb+resource. It distinguishes from sibling tools like register_diagnostic and register_intervention by emphasizing predicted_effects commitment.

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?

Explicitly provides when to use (propose hypothesis for a problem), prerequisite (registered diagnostic), and alternatives ('Call `recall` first', 'pass `supersedes`'). Also states consequences if not followed ('rejected otherwise').

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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