oraclaw-mcp-server

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

Adds key behavioral trait 'Provably optimal' and solver identity (HiGHS) beyond empty annotations, but omits failure modes, auth, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely terse with no filler; front-loaded with technical paradigm and solver name; every clause delivers value.

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

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

Adequate for tool selection but lacks return value description needed given no output schema exists, and omits complexity warnings for nested constraint objects.

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

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

Schema coverage is 75% (high), and description adds no parameter-specific semantics, meeting baseline expectations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states LP/MIP/QP optimization using HiGHS and lists specific use cases, distinguishing from sibling optimizers by mathematical paradigm.

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

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

Provides example domains (budget, scheduling, planning) but lacks explicit when/when-not guidance versus siblings like solve_schedule or optimize_cmaes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.