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dsh_run_task

Dispatch autonomous coding tasks to a headless AI worker that edits files, executes bash commands, and returns structured results without consuming front-end tokens.

Instructions

Dispatch an autonomous coding task to DeepSeek Harness in headless mode using the configured local/free model. The worker operates directly on the specified repository, reasons autonomously, creates/edits files, executes bash commands, tracks git diffs, and returns structured execution results with zero front-end token burn.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdYesAbsolute path to the repository/directory the worker should operate in.
taskYesClear, explicit instructions for the task: files to edit/create, requirements, constraints, and test commands.
verboseNoWhether to include full raw stdout/stderr in the response (default: false).
timeoutMsNoMax execution time in milliseconds (default: 1800000 = 30 minutes).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses that the worker mutates the repository, creates/edits files, executes bash commands, tracks git diffs, and returns structured results. It stops short of noting risks, permissions, or reversibility, but the core behavioral traits are explicit.

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 one dense, front-loaded sentence that starts with the action and resource, then packs in the behavioral details without redundancy. It is efficient, though a slight structural split would improve scannability.

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?

For a complex mutation tool with no output schema and no annotations, the description covers the critical operational aspects: mode, target repo, autonomy, side effects, and result type. It leaves some return-format details vague ('structured execution results') and does not mention timeout/failure behavior, but the main agent-relevant context is present.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all four parameters well. The description adds general context about what the task worker can do, but it does not add per-parameter meaning beyond what the input schema provides.

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 uses a specific verb ('Dispatch') and names the exact resource: an autonomous coding task to DeepSeek Harness in headless mode. It also enumerates observable effects (creates/edits files, executes bash commands, tracks git diffs) that clearly set it apart from the sibling status/management tools.

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

Usage Guidelines3/5

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

The intended use case is clear: run autonomous coding tasks against a repository in headless mode. However, the description never explicitly states when to use this tool versus alternatives or when not to use it, leaving the distinction from sibling tools implicit rather than direct.

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