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Anselmoo

mcp-zen-of-languages

by Anselmoo

Generate agent tasks

generate_agent_tasks
Read-onlyIdempotent

Convert zen violations into structured agent task lists for automated remediation. Scan a repository for language-specific issues and generate actionable tasks filtered by severity.

Instructions

Convert zen violations into structured agent task lists for automated remediation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languagesNoRestrict scanning to these languages. Omit to analyse only Python files by default. Default to None.
repo_pathYesAbsolute path to the repository to scan. All eligible source files are discovered recursively.
min_severityNoSeverity floor (1-10 scale). Violations below this threshold are excluded from the task list. Default to 5.
enable_external_toolsNoOpt-in execution of allow-listed external tools while gathering repository analysis. Default to False.
allow_temporary_runnersNoPermit temporary-runner fallback strategies for external tools. Default to False.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tasksYes
projectYes
roadmapNo
clustersNo
total_tasksYes
health_scoreYes
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 no extra behavioral context (e.g., prerequisites, side effects) beyond the core conversion purpose, so it meets the lowered bar but does not enrich beyond it.

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, concise sentence that front-loads the verb and purpose. Every word earns its place, with no unnecessary fluff or repetition of schema details.

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

Completeness4/5

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

Given the tool's read-only, idempotent nature and the presence of an output schema, the short description is largely sufficient. It misses some workflow context (e.g., that it likely consumes results from analyze_zen_violations), but this is not critical for selection and invocation.

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 all five parameters are already documented in the input schema. The description does not add any additional parameter meaning or usage context, matching the baseline for full coverage.

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 a specific verb ('Convert') and clearly states the resource ('zen violations') and output ('structured agent task lists'). This distinguishes it from sibling tools like analyze_zen_violations (which likely analyzes violations) and generate_prompts (which generates prompts), making its unique role clear.

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 the tool is used after obtaining zen violations, but it does not explicitly state when to use it versus alternatives or provide workflow context. No exclusions are mentioned, and the positioning relative to sibling tools is left to inference.

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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