Skip to main content
Glama
sinagilassi

PyThermoCalcDB-NASA-MCP

by sinagilassi

check_yaml_reference

Validate YAML reference content for correct formatting and compatibility with pyThermoDB, ensuring it works as a valid input for thermodynamic calculations.

Instructions

Validate pythermodb YAML reference content for use with pyThermoDB.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yaml_contentYesThe YAML content to be checked as a reference.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description bears full responsibility for disclosing behavior. The term 'validate' implies a non-destructive check, but it fails to state whether the tool modifies state, requires special permissions, or what side effects (if any) occur. The description is insufficiently transparent.

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?

The description is a single sentence with no wasted words. It efficiently conveys the core action and target resource, earning its place without redundancy.

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

Completeness3/5

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

Given the simple tool signature (one parameter) and the existence of an output schema (which likely describes the validation result), the description is minimally adequate. However, it does not explain what the output represents (e.g., success, errors) or how validation failures are communicated, leaving some ambiguity.

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 the parameter is fully described in the schema. The description adds no additional meaning beyond the schema; it merely restates the tool's purpose. Per the rubric, baseline 3 applies when schema coverage is high, and the description does not contribute further.

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 uses the specific verb 'validate' and identifies the resource as 'pythermodb YAML reference content'. It clearly distinguishes this tool from its sibling calculation tools (all calc_*), making the purpose unmistakable.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus its siblings or any alternatives. It does not state that validation should precede calculations or specify any prerequisites or exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sinagilassi/PyThermoCalcDB-NASA-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server