pyResToolbox MCP Server

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

With no annotations provided, the description carries full burden. It discloses the calculation method (Standing 1947 correlation) and return format (dictionary with Rs, method, units, inputs), which is helpful. However, it doesn't mention error conditions, numerical precision, validation of inputs, or whether the calculation is deterministic - important behavioral aspects for a computational tool.

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?

The description is well-structured with purpose, method, usage context, return format, and parameter explanation in logical order. Every sentence earns its place, though the Args/Returns section could be slightly more integrated with the main description rather than appearing as separate documentation blocks.

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?

Given this is a computational tool with no annotations but with an output schema (implied by 'Returns' section), the description provides good coverage. It explains what the tool does, when to use it, the method employed, return format, and parameter meanings. The main gap is lack of error handling or edge case information.

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?

The schema description coverage is 0% (no parameter descriptions in schema), so the description must compensate. It successfully explains that parameters represent 'bubble point parameters (API, temperature, bubble point, gas gravity)' and clarifies the purpose of each in the context of the calculation. This adds substantial meaning beyond the bare schema.

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?

The description clearly states the specific verb 'calculate' and resource 'solution GOR at bubble point', explicitly naming the Standing correlation method. It distinguishes from sibling tools like 'oil_bubble_point' (which likely calculates pressure rather than GOR) and 'oil_solution_gor' (which may calculate GOR at other conditions).

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?

The description provides clear context for when to use this tool: 'when you know the bubble point pressure and need to calculate the corresponding solution GOR.' However, it doesn't explicitly mention when NOT to use it or name specific alternative tools from the sibling list that might be relevant for different scenarios.

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