mcp-zen-of-languages

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Analyze repository (auto-routing)

analyze_batch_auto

Read-onlyIdempotent

Automatically analyzes repositories for language-specific violations, returning all results for small repos or paginating large ones. Use the returned cursor to load the next page when more violations exist.

Instructions

Smart entry point for LLM agents: automatically decides between returning all violations at once (small repos) or paginating (large repos). Pass the returned cursor back to continue pagination if has_more is true. Prefer this over manually choosing between analyze_repository and analyze_batch.

Input Schema

TableJSON Schema

Name	Required	Description
`path`	Yes	Absolute or relative path to the repository root.
`cursor`	No	Opaque continuation token from a previous call. Omit or pass ``None`` to start from the first page. Default to None.
`language`	Yes	Language identifier to restrict analysis (e.g. ``"python"``).
`max_files`	No	Cap on the number of files to analyse. Default to 100.
`max_tokens`	No	Approximate token budget for the ``violations`` payload. When all violations fit, they are returned in full; otherwise the first page is returned. Default to 8000.
`enable_external_tools`	No	Opt-in execution of external linters. Default to False.
`allow_temporary_runners`	No	Permit temporary-runner strategies. Default to False.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`page`	Yes
`cursor`	Yes
`has_more`	Yes
`violations`	Yes
`files_total`	Yes
`files_in_page`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.8.1Aug 5, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint=false), the description discloses the auto-routing behavior between small and large repos, and the cursor-based pagination mechanism. This adds valuable context about how the tool behaves at runtime, which annotations alone do not provide.

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 two sentences, front-loaded with the core purpose ('Smart entry point'), and every phrase earns its place. It efficiently conveys the auto-routing logic, pagination guidance, and sibling preference without redundancy.

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

Completeness5/5

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

Given the tool's complexity (7 params, 2 required), the description sufficiently covers the essential behavior and usage. Since an output schema exists, return values like has_more are already defined; the description adds the necessary context about auto-routing and pagination, making it complete for an agent to invoke correctly.

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?

The schema description coverage is 100%, so all parameters are already described. The description adds minimal parameter-level meaning; it only reinforces the cursor's role in pagination ('Pass the returned cursor back') without introducing new details beyond the schema. Baseline 3 is appropriate.

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 purpose: 'automatically decides between returning all violations at once (small repos) or paginating (large repos).' It distinguishes from siblings by explicitly recommending it over analyze_repository and analyze_batch, making it a distinct and purposeful entry point.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'Prefer this over manually choosing between analyze_repository and analyze_batch.' It also explains the pagination flow with 'Pass the returned cursor back to continue pagination if has_more is true,' giving clear step-by-step instructions for using the tool correctly.

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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