petropt/petro-mcp

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool calculates but doesn't describe output format, units of results, error conditions, computational complexity, or whether it's a pure calculation (likely read-only) versus having side effects. The mention of 'status check' for current_rate_mcfd hints at diagnostic use but isn't elaborated.

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 a purpose statement followed by a parameter glossary. Every sentence adds value: the first establishes context and calculation, the second defines the model, and the parameter explanations are essential given 0% schema coverage. It could be slightly more front-loaded by integrating key parameter context earlier.

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 the complexity (7 parameters, engineering calculation), no annotations, but with an output schema (implied by context signals), the description is moderately complete. It covers parameter semantics well but lacks behavioral context like output interpretation or error handling. The output schema existence reduces the need to describe return values, but more guidance on usage scenarios would help.

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 description coverage is 0%, so the description must compensate. It provides clear semantic meaning for all 7 parameters, explaining what each represents (e.g., 'Wellhead flowing pressure in psi'), including defaults and optional status. This adds significant value beyond the bare schema, though it doesn't detail validation ranges or interdependencies between parameters.

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 purpose: 'Calculates the minimum gas velocity and flow rate needed to continuously lift liquids from a gas well using the droplet model.' It identifies the exact calculation (Turner et al. critical rate), the resource (gas well liquid unloading), and the method (droplet model). This distinguishes it from sibling tools like calculate_coleman_critical which likely uses a different model.

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 implies usage context through the mention of 'gas well liquid unloading' and 'droplet model,' suggesting this tool is for analyzing liquid loading in gas wells. However, it doesn't explicitly state when to use this versus alternatives like calculate_coleman_critical or other gas flow tools, nor does it mention prerequisites or exclusions.

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