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solve_problem

Solves complex problems using structured chain-of-thought reasoning with adaptive parsing, retry logic, and token budget management.

Instructions

Solves a problem using strict Agentic Chain-of-Thought with adaptive parsing, retry logic, token budgeting (via tiktoken), and configurable model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe problem to solve.
historyNoPrevious CoT steps to build on for multi-turn reasoning
resultSchemaNoOptional JSON schema to validate the result field against.
Behavior4/5

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

No annotations provided, so description bears full burden. It discloses key behaviors: adaptive parsing, retry logic, token budgeting, configurable model. Does not disclose error handling or return format but adds value beyond schema.

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?

Single sentence, well-structured, front-loaded with core purpose. No filler content.

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

Completeness2/5

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

Description lacks output specification (no output schema). Given complexity of 3 params and nested objects, description should clarify return value or result format. Incomplete.

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 coverage is 100%, so baseline 3 applies. Description adds no additional parameter semantics beyond what schema provides.

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 verb 'solves' and the resource 'problem', specifying the method 'Agentic Chain-of-Thought' with technical details like 'adaptive parsing, retry logic, token budgeting'. This distinguishes it from generic problem-solving tools.

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

Usage Guidelines3/5

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

Implied usage for problems requiring step-by-step reasoning, but no explicit when-to-use or when-not-to-use guidance. No sibling tools are provided for comparison, so no alternative recommendations.

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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