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local_lint_summary

Read-only

Summarize linter, test, and CI output using a local model. Reads large files server-side, groups errors by file and rule, and returns a concise summary without consuming Claude's context.

Instructions

PREFIERE esta tool en vez de leer el archivo con Read cuando el archivo es grande (>200 líneas / >10 KB) y solo necesitas un resumen agrupado, no el contenido literal. Si ejecutaste un comando cuya salida es larga, vuélcala a un archivo y pasa 'path'.

Resume salida de linters/tests/CI con un modelo local, sin gastar contexto de Claude.

Pensada para logs largos y ruidosos (ESLint, clippy, pytest, tsc, CI). Pasa 'path' y el
archivo se lee del lado del servidor, de modo que el log completo NO entra al contexto de
Claude: solo vuelve un resumen agrupado por archivo con el conteo por tipo de error/regla y
lo más importante primero. Alternativamente pasa 'text'. Enruta al modelo mecánico (corto) o
al de contexto largo (largo) automáticamente.

Args:
    path: Ruta al archivo de salida de lint/tests (leído server-side). Usa esto o 'text'.
    text: Salida de lint/tests como texto.
    max_words: Longitud máxima del resumen en palabras.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo
textNo
max_wordsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description adds that the file is read server-side, returns a grouped summary with counts, and routes to short/long context model automatically. This provides useful behavioral context beyond 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 well-structured with a strong recommendation upfront, followed by function explanation and parameter docs. It is slightly verbose but effective, around 10 sentences. No waste.

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 (3 parameters, output schema exists), the description provides complete context: what it does, when to use, parameters, and output nature (grouped summary by file). An output schema is present, so not detailing return values is acceptable.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description fully documents all three parameters: path (server-side read to avoid Claude context), text (direct input), and max_words (summary length with default 200). It adds significant meaning and usage context.

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 summarizes lint/test/CI output using a local model, saving Claude context. It lists specific use cases (ESLint, clippy, pytest, tsc, CI) and distinguishes from sibling tools like Read.

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

Usage Guidelines4/5

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

The description explicitly recommends this tool over Read for large files (>200 lines / >10 KB) needing a grouped summary. It advises dumping long command output to a file and using 'path'. It lacks explicit 'when not to use', but the guidance is strong.

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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