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Anselmoo

mcp-zen-of-languages

by Anselmoo

Generate remediation prompts (v2)

generate_prompts
Read-onlyIdempotent

Generate remediation prompts from code and language inputs, with MCP-first guidance and versioned prompt semantics for language-specific best practices.

Instructions

Generate remediation prompts with MCP-first guidance metadata and v2 versioned prompt semantics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesSource code to analyse.
languageYesProgramming language identifier.
project_asNoProjection-family target when ``perspective`` is ``projection``.
perspectiveNoRequested analysis perspective. Default to ``PerspectiveMode.ALL``.all
enable_external_toolsNoEnable external tools. Default to False.
allow_temporary_runnersNoAllow temporary runners. Default to False.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
big_pictureNo
file_promptsNo
generic_promptsNo
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that prompts include 'MCP-first guidance metadata' and 'v2 versioned prompt semantics,' which gives a hint about output structure but lacks concrete details about behavior such as return format or side effects. No contradiction with annotations.

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 a single sentence and is efficient in length. However, the specialized terms 'MCP-first' and 'v2 versioned' may not be universally understood and reduce clarity, though they do not add unnecessary bulk.

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 presence of an output schema and fully documented parameters lowers the burden on the description. Yet the description does not explain the broader context of remediation prompts, when to invoke this tool, or how the various parameters (e.g., perspective, project_as) interact. Given the large sibling set, additional context would be valuable.

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?

All six parameters are fully described in the schema (100% coverage), so the description does not need to elaborate on parameter semantics. The description itself mentions no parameters, and the schema already handles meaning, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Generate') and resource ('remediation prompts'), which is specific and matches the name. However, it does not explicitly differentiate from sibling tools like 'generate_agent_tasks' or the analysis tools, and the added jargon ('MCP-first guidance metadata', 'v2 versioned prompt semantics') may confuse rather than clarify.

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 the many sibling tools (e.g., analyze_zen_violations, generate_agent_tasks). It does not mention prerequisites, typical use cases, or exclusions, leaving the agent to infer usage solely from the name.

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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