Planning System MCP Server

resolve_decision

Resolve pending decisions by approving, deferring, or rejecting. Include optional notes or select presented options.

Instructions

Resolve a pending decision. action is 'approve', 'defer', or 'reject'. Use this from Cowork artifact buttons or after a human responds in chat.

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": {
      "decision_id": "<decision_id>",
      "plan_id": "<plan_id>",
      "action": "<action>"
    },
    "name": "resolve_decision"
  }
}

Input Schema

Name	Required	Description
`decision_id`	Yes
`plan_id`	Yes	Plan that owns the decision (required by API path)
`action`	Yes
`message`	No	Optional resolution note
`selected_option`	No	When the decision presented options, which was chosen

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.8.1Apr 29, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states the action types and optional fields. Missing details: side effects of resolution, permission requirements, state changes, or what happens to the decision (e.g., removed from pending list). The description is too brief to inform an agent about important behaviors.

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 extremely concise: two sentences that clearly state purpose and usage context. Every sentence is necessary and contributes meaning. No fluff.

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

Completeness2/5

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

Given the tool has 5 parameters, no output schema, and no annotations, the description is incomplete. It lacks information about the return value, error handling, the role of plan_id (required by API path), and the consequences of resolving a decision. The description covers only the basic action and partial usage context, leaving significant gaps for an AI agent.

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 coverage is 60%, and the description adds value by listing the action enum values, which are also in the schema. However, it does not explain the decision_id parameter (missing schema description) or add extra context beyond the schema for plan_id, message, or selected_option. It meets the baseline but does not compensate for gaps.

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 (resolve) and the object (pending decision), and provides specific use cases (from Cowork artifact buttons or after human response). It effectively distinguishes from siblings like queue_decision.

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 explicitly mentions when to use: from Cowork artifact buttons or after a human responds in chat. It does not mention when not to use or alternatives, but the context is clear and provides good guidance for a specific scenario.

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