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load_prompt

Load a specific prompt or prompt section on demand for ADR generation, analysis, and deployment. Reduces token consumption by loading only when needed.

Instructions

Load a specific prompt or prompt section on-demand. Part of CE-MCP lazy loading system that reduces token usage by ~96% by loading prompts only when needed. Use this to retrieve prompt templates for ADR generation, analysis, deployment, and other operations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionNoSpecific section within the prompt to load. If not provided, loads the entire prompt. Available sections depend on the prompt.
promptNameYesName of the prompt to load (e.g., "adr-suggestion", "deployment-analysis", "environment-analysis", "research-question", "rule-generation", "analysis", "security")
estimateOnlyNoIf true, returns only token estimate without loading the full prompt content
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. It explains the lazy loading behavior and the estimateOnly feature, but it does not disclose whether the tool is read-only, has any side effects, or requires specific permissions. The description lacks important behavioral context beyond basic functionality.

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 two sentences: one defining purpose, one providing usage context. It is concise and direct with no unnecessary words. Could potentially be more structured (e.g., bullet points for use cases), but it is efficient.

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

Completeness2/5

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

The tool has 3 parameters, no output schema, and no annotations. The description covers the purpose and usage context but does not describe return values, error conditions, or performance characteristics. For a tool that loads content, details about what is returned (e.g., string content) are missing.

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 description coverage is 100%, so the baseline is 3. The description adds some value for the 'section' parameter ('Available sections depend on the prompt'), but for other parameters it largely repeats the schema descriptions. No significant additional meaning beyond schema.

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's action ('Load a specific prompt or prompt section'), the resource ('prompt'), and its role in the lazy loading system. It also lists specific use cases (ADR generation, analysis, deployment) which distinguish it from sibling tools that perform other functions.

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 suggests using the tool 'to retrieve prompt templates for ADR generation, analysis, deployment, and other operations' and mentions it's part of a lazy loading system to reduce token usage. However, it does not provide explicit guidance on when not to use this tool or alternatives to consider.

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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