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mcp-ai-workforce

workforce_delegate

Delegate software engineering tasks to an autonomous worker that safely edits files under guardrails and returns a summary, cutting token costs.

Instructions

Delegate a software engineering task to an autonomous ReAct worker agent.

The worker operates with filesystem tools under strict guardrails to inspect, read, modify, and create workspace files safely.

Args: task_prompt: Comprehensive description of the task or feature to implement. target_files: Optional list of relevant workspace-relative file paths. model: OpenRouter model to utilize (defaults to DEFAULT_MODEL). timeout_seconds: Execution timeout limit in seconds (default: 300).

Returns: str: Final completion summary or diagnostic output from the worker agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
task_promptYes
target_filesNo
timeout_secondsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses that the worker uses filesystem tools to inspect, read, modify, and create files under 'strict guardrails,' and that it returns a completion summary or diagnostic. It does not detail side effects, failure modes, or cost/time implications, but the core mutation behavior is explicit.

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-organized with a one-sentence purpose, a brief safety/behavior note, an Args list, and a Returns line. No filler; all sentences add information.

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?

The description covers purpose, all parameters, behavior, and return type, and an output schema exists. The only gap is the absence of any comparison to sibling tools or explicit usage boundaries, so it is not quite as complete as it could be.

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?

Although the schema has no property descriptions, the tool description's Args section explains every parameter: task_prompt content, target_files as optional workspace-relative paths, model as an OpenRouter model with a default, and timeout_seconds with its default. This fully compensates for the schema's 0% description coverage.

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 specific action ('Delegate a software engineering task') and a specific target ('autonomous ReAct worker agent'). It clearly distinguishes from siblings like workforce_audit_diff and workforce_models_status, which are about auditing diffs and checking model status, not delegating implementation work.

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 description implies the tool is for delegating implementation tasks, but it gives no explicit when-to-use or when-not-to-use guidance and never references the sibling tools. There is no exclusion criteria or alternative selection advice.

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