petropt/petro-mcp

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

With no annotations provided, the description carries the full burden. It discloses the tool's scope (multiple decline models) and domain-specific context (well production analysis), but doesn't mention computational behavior, error handling, output format, or performance characteristics. It adequately describes what the tool does but lacks operational transparency.

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, model support list, and parameter documentation. While comprehensive, it's appropriately sized for an 8-parameter technical tool. Some sentences could be tighter (e.g., the model list runs long), but overall it's efficient and front-loaded with the core purpose.

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 tool's complexity (8 parameters, multiple mathematical models) and the presence of an output schema (which handles return values), the description provides strong context. It covers purpose, supported models, and detailed parameter semantics. The main gap is lack of explicit usage guidelines versus sibling tools, but otherwise it's quite complete for a calculation tool.

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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter explanations. Each of the 8 parameters gets clear semantic meaning, units, typical ranges, and model-specific usage notes (e.g., 'b: Arps b-factor (0=exponential, 1=harmonic, 0-2=hyperbolic)'). This adds substantial value 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 tool's purpose: 'Calculate Estimated Ultimate Recovery using decline parameters.' It specifies the exact calculation (EUR) and the domain (decline analysis), distinguishing it from sibling tools like 'calculate_well_economics' or 'eur_distribution' which have different focuses.

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 by listing supported models (Arps, modified hyperbolic, Duong) and mentioning 'unconventional/shale wells' for the Duong model. However, it doesn't explicitly state when to use this tool versus alternatives like 'fit_decline' or 'forecast_advanced_decline', nor does it provide prerequisites or exclusions.

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