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auspham

Copilot Memory MCP

by auspham

run_long_command

Run long shell commands like builds, installs, or test suites and capture complete output, with configurable timeouts and regex-based early stopping.

Instructions

Run a long-running shell command and capture its full output.

Unlike monitor_command (which re-runs a command repeatedly), this runs
ONE command and streams its output until it finishes or times out.

Use for: builds, installs, database migrations, test suites, any single
command that takes a while.

Examples:
    - Build: command="npm run build", timeout_seconds=300
    - Tests: command="pytest -v", stop_pattern="FAILED|passed"
    - Install: command="apt install -y nginx", timeout_seconds=120

Args:
    command: Shell command to run.
    timeout_seconds: Max seconds to wait (default 600 = 10 min, max 3600).
    tail_lines: Number of output lines to return from the end (default 100).
    stream_check_interval: Seconds between progress checks (default 5).
    stop_pattern: Regex — kill the process early if output matches this.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commandYes
tail_linesNo
stop_patternNo
timeout_secondsNo
stream_check_intervalNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral disclosure burden. It covers one-shot execution, streaming until completion or timeout, progress-check intervals, early termination via stop_pattern, and tail output. However, 'capture its full output' vs. 'tail_lines' returning only the end of output creates a minor ambiguity about what exactly is returned.

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 well-structured: a one-line summary, a comparison with the sibling, a use-case list, concrete examples, and a clear Args section. Every sentence is informative; the examples use realistic values, and the section headers make scanning easy.

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 5 parameters, no annotations, and an output schema handling the return shape, this description covers everything an agent needs to invoke the tool correctly: what it does, when to use it, how it differs from the closest sibling, parameter details with defaults, and example usage. There are no significant gaps.

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%, so the description alone must explain all 5 parameters. It does so thoroughly: each arg gets a meaning, default value, and in some cases a max or format (e.g., timeout_seconds default 600/max 3600, stop_pattern as regex that kills the process). Example command pairings make the semantics even clearer.

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 opens with a precise verb+resource statement: 'Run a long-running shell command and capture its full output.' It explicitly distinguishes itself from monitor_command, so an agent can immediately recognize this tool's unique role among siblings.

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?

It names the specific alternative (monitor_command) and explains the key difference: this runs ONE command, whereas that re-runs repeatedly. It also lists concrete use cases (builds, installs, migrations, test suites), providing 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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