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fastmcp-me

MCP-Airflow-API

by fastmcp-me

get_prompt_template

Get a structured prompt template for guiding LLM interactions with Apache Airflow. Optionally filter by section and choose summary or detailed mode to match your needs.

Instructions

[Tool Role]: Provides comprehensive prompt template for LLM interactions with Airflow operations.

Args: section: Optional section name to get specific part of template mode: Optional mode (summary/detailed) to control response verbosity

Returns: Comprehensive template or specific section for optimal LLM guidance

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
sectionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention whether the tool is read-only, has side effects, requires permissions, or handles invalid inputs. For a tool that returns a template, some transparency about safety or error behavior would be expected; the description offers none beyond the output description.

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

Conciseness4/5

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

The description is structured with sections for role, args, and returns, making it scannable. It is concise, but there is slight redundancy with 'comprehensive' appearing twice. Overall, every sentence contributes to understanding, and it avoids unnecessary filler.

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?

The description provides basic purpose, parameter explanations, and return summary, but lacks details such as available section names, behavior for invalid modes/sections, or interaction with other tools. Given the tool's simplicity and the presence of an output schema (not shown to the agent), it is adequate but has clear gaps in edge-case handling and operational specifics.

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

Parameters4/5

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 explains both parameters: 'section' as 'Optional section name to get specific part of template' and 'mode' with explicit allowed values (summary/detailed) to control verbosity. This adds meaningful guidance beyond the raw schema, though it does not enumerate available sections or default behavior details.

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 clearly states the tool provides a 'comprehensive prompt template for LLM interactions with Airflow operations,' using a specific verb+resource structure. This distinguishes it from all sibling tools, none of which mention prompt templates. The purpose is unambiguous and directly aligned with the tool name.

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

Usage Guidelines3/5

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

The description implies when to use the tool ('for optimal LLM guidance' in the Returns line) but does not explicitly state usage scenarios, prerequisites, or exclusions. There are no alternative prompt-template tools among siblings, so differentiation is not needed, but the guidance is only implied, not directly stated.

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

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