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lgh_log

Read-onlyIdempotent

Retrieve recent server log entries from the local log file, optionally filtered by severity level, to diagnose past failures or unexpected behavior. Returns a JSON array of log records.

Instructions

Read the LGH server's runtime log from ~/.localgithub/logs/server.jsonl and return the most recent entries as a JSON array of {ts, level, msg, component} objects (an empty array if no log file exists yet). A level filter keeps only entries at exactly that severity, so level=ERROR surfaces just the failures. Use this to investigate why a push or the server misbehaved in the past: lgh_status only shows the current health snapshot, while lgh_log shows the history that led to it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
levelNoFilter by log level (DEBUG, INFO, WARN, ERROR)
limitNoNumber of log entries to return (default: 20)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv0.1.1
    • addedInput schema / properties / level / enum
      Added value: +[
      +  "DEBUG",
      +  "INFO",
      +  "WARN",
      +  "ERROR"
      +]
  2. First observedv0.1.0

TDQS

A4.8/5.0
Behavior4/5

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

Descriptors in the description (reading log, empty array if no file) align with the annotations (readOnly, idempotent, not destructive). The description adds useful behavioral details about output format and empty-case handling, though it doesn't need to restate the annotation flags.

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 compact and front-loaded: it states the core action in the first sentence, then adds the level filter and use-case context. No unnecessary words or 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?

Despite lacking an output schema, the description explicitly defines the return shape ('JSON array of {ts, level, msg, component} objects') and the empty-array behavior. It also mentions the sibling tool lgh_status for contrast, providing enough context for correct invocation.

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?

The schema already documents both parameters (level with enum values, limit with description). The description reinforces the meaning of level ('exactly that severity') and its purpose, which is sufficient.

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?

States a specific verb ('Read') and a specific resource ('the LGH server's runtime log'). Clearly distinguishes from lgh_status by noting that lgh_log shows history rather than current health.

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?

Explicitly says to use when investigating why a push or the server misbehaved in the past, and contrasts with lgh_status which shows the current health snapshot. This gives clear when-to-use guidance.

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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