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agent-coordination-mcp

agent-coordination-mcp

by HappyMonkeyAI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

assign_task

Assign a project task to a CLI agent, storing the agent name, task ID, and instruction for project coordination.

Instructions

Record that a CLI agent has been assigned a project task.

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": {
      "project_path": "<project_path>",
      "agent_name": "<agent_name>",
      "task_id": "<task_id>",
      "instruction": "<instruction>"
    },
    "name": "assign_task"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_pathYes
agent_nameYes
task_idYes
instructionYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

No annotations provided. The description only states the action without disclosing side effects, authorization needs, error conditions, or whether it overwrites existing assignments. Minimal behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence is concise but overly terse given the lack of schema descriptions. Front-loaded with purpose, but missing essential details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 4 required parameters, no schema descriptions, no annotations, and an output schema that is not explained, the description is severely incomplete for an AI agent to use correctly.

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

Parameters1/5

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

Schema description coverage is 0%, but the description does not explain any parameter meanings (e.g., what 'instruction' or 'agent_name' represent). Fails to add value beyond the schema titles.

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 action: recording that a CLI agent has been assigned a project task. It uses a specific verb and resource, and implicitly distinguishes from siblings like 'update_assignment_status' or 'list_assignments'.

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?

No guidance on when to use this tool versus alternatives (e.g., 'update_assignment_status'), no prerequisites, and no context about 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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