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io.github.RohitYajee8076/backburner

io.github.RohitYajee8076/backburner

by RohitYajee8076
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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task_result

Retrieve a task's combined stdout and stderr output, with optional trailing line limit. Works for completed and running tasks to view live progress.

Instructions

Get a task's captured output (stdout+stderr merged).

Args: task_id: The task to inspect. Works for finished AND still-running tasks, so you can peek at live progress. tail_lines: How many trailing lines of output to return.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "task_id": "<task_id>"
    },
    "name": "task_result"
  }
}

Input Schema

NameRequiredDescriptionDefault
task_idYes
tail_linesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals that stdout and stderr are merged, that it supports both finished and running tasks (enabling live progress peeking), and that tail_lines limits the output. This is useful behavioral context beyond a simple 'get output' statement.

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 concise and well-structured: a one-line summary followed by a clear Args section. Every sentence adds value, with no redundant or filler text. It is appropriately sized for the tool's simplicity.

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 no output schema and no annotations, the description covers the essential behaviors: what output is returned, how the output is merged, and the ability to inspect running tasks. It does not specify the exact return format, but the description is sufficiently complete for a straightforward output-fetching tool.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It does so by explaining both parameters: task_id is 'the task to inspect' and can be used on running tasks, and tail_lines specifies 'how many trailing lines of output to return.' This adds meaningful semantics beyond the bare schema properties.

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: 'Get a task's captured output (stdout+stderr merged).' The verb 'Get' plus the resource 'task's captured output' is specific, and the scope (stdout+stderr) distinguishes it from sibling tools like task_status or cancel_task.

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 provides clear usage context by noting it works for 'finished AND still-running tasks, so you can peek at live progress.' This implies when to use it (to inspect output, especially for in-progress tasks), though it does not explicitly name alternative tools or state when not to use it.

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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