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by Koredotcom
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TypeScript
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debug_explain_decision

Trace agent decisions by explaining a specific event with contextual details. Filter by session, turn, or event ID to see the reasoning behind any choice.

Instructions

[Arch Debug] Arch connects to live sessions, traces failures, and inspects execution state. Get detailed explanation of a decision event with surrounding context. Helps understand why the agent made a choice.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "debug_explain_decision"
  }
}

Input Schema

NameRequiredDescriptionDefault
turnNoGet all decisions for a specific conversation turn
typeNoFilter by decision type (handoff, completion, gather_extraction, etc.)
lastNNoNumber of recent decision entries to return
eventIdNoSpecific event ID to explain
sessionIdNoSession ID (uses active session if not specified)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.0

TDQS

C2.6/5.0
Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention whether the operation is read-only, any side effects, permission requirements, rate limits, or what the response contains. The generic opening about Arch connecting to live sessions offers no specific behavioral insight about this tool.

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 concise at three sentences. It front-loads a general Arch context, then states the tool's specific purpose, and finishes with a value statement. The first sentence is generic across debug tools and could be trimmed, but overall the length is appropriate and readable.

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?

Despite having 5 parameters and no output schema, the description fails to explain what the tool returns, how the parameters relate to each other (e.g., turn vs. eventId), or any prerequisites like an active session. The meaning of 'decision event' is not defined, and there is no guidance on expected output structure, making the tool difficult to invoke correctly.

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%, with all five parameters documented in the input schema. The description adds no additional parameter semantics, but the baseline score of 3 is appropriate since the schema already explains each parameter's purpose adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get detailed explanation') and the resource ('a decision event') and even explains the underlying goal ('Helps understand why the agent made a choice'). While it doesn't explicitly contrast with sibling debug tools, the purpose is specific enough to differentiate from general tracing or state inspection tools.

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?

There is no explicit guidance on when to use this tool versus alternatives like debug_get_flow_graph or debug_diagnose. The description implies it is for explaining decision events, but it lacks any exclusions or references to sibling tools that might handle similar cases. This leaves the agent without direction on tool selection.

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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