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Conjecture relations (Ramanujan Machine)

conjecture_relation
Read-onlyIdempotent

Find conjectured algebraic relations for real constants using PSLQ and continued-fraction search when standard identification returns unidentified.

Instructions

Conjecture relations for a real constant — Ramanujan-Machine style: PSLQ over a rich basis + continued-fraction/recurrence search; every candidate numerically VERIFIED to >= 25 digits but NOT proved (provenance 'conjectured_relation'). Use when identify_constant returns UNIDENTIFIED. Args: value (decimal string, MANY digits), max_terms (default 16), cf_depth (default 200).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesthe real constant as a decimal string (give MANY digits; PSLQ/CF search needs >16)
max_termsNomax PSLQ basis vector length (default 16; cost grows fast)
cf_depthNocontinued-fraction evaluation depth (default 200)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
foundYes
integer_relationsYes
continued_fractionsYes
simple_continued_fractionNo
noteNo
Behavior5/5

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

Annotations indicate readOnly and idempotent; description adds algorithms used (PSLQ, CF), verification threshold (>=25 digits), and provenance status (conjectured). 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?

Two efficient sentences, front-loaded with action and key information. Every sentence adds value without redundancy.

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?

Covers purpose, usage condition, algorithm, verification state, and parameter hints. Could mention potential for no results or runtime cost, but overall sufficient given output schema exists.

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

Parameters4/5

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

Schema coverage is 100% but description adds useful context: 'MANY digits', 'cost grows fast' for max_terms, default values. Enhances understanding beyond schema.

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?

Clearly states the tool's purpose: finding conjectured relations via PSLQ and continued-fraction methods, with numerical verification but not proof. Usage condition 'when identify_constant returns UNIDENTIFIED' distinguishes it from sibling identify_constant.

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?

Explicitly states when to use: after identify_constant returns UNIDENTIFIED. Does not explicitly mention when not to use or alternatives, but context implies other sibling tools for different tasks.

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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