LocalREPL MCP Server

by angrysky56

Overview Schema Source Related Servers Discussions Admin

Python

Local

command_stats

Analyze command log usage by aggregating metrics and returning top entries for a given time period.

Instructions

Aggregate metrics across the command log.

    Args:
        since: Window to analyze (ISO or '1h'/'1d' etc). None = all time.
        top_n: How many entries to return in each top-list.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "command_stats"
  }
}

Input Schema

Name	Required	Description	Default
`since`	No
`top_n`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 12, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description bears full burden. It indicates aggregation and top-lists but omits behavioral traits like read-only status, side effects, or rate limits. The term 'aggregate' is vague regarding safety or mutation.

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 concise with a single-purpose sentence and a clear parameter list. It avoids fluff, though structure could be slightly improved (e.g., separating description from parameter details). Still efficient.

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 only 2 parameters with good semantic explanations, no output schema, and no annotations, the description is fairly complete for an aggregation tool. It lacks output details but is sufficient for selecting the tool.

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 description coverage is 0%, yet the description adds significant meaning: 'since' is explained (ISO or shorthand, default all time) and 'top_n' (number of entries per top-list). This fully compensates for the schema gap.

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 'Aggregate metrics across the command log', specifying both the verb (aggregate) and resource (command log metrics). It distinguishes well from sibling tools like query_command_history (raw history) and tag_command (tagging), making purpose unique.

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 does not provide explicit guidance on when to use this tool versus alternatives. It only implies use for aggregated stats, but no when-to-use, prerequisites, or exclusions are given, leaving usage ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.