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gaztrabisme

deepseek-subagent-mcp

by gaztrabisme

dsh_transcript

Retrieve the tail of a subagent's activity log to inspect progress or diagnose failures. Includes tool calls, assistant messages, and optional raw response data from a running or completed run.

Instructions

Show what a subagent actually did during a run.

Returns the tail of its activity log — tool calls, assistant messages, turn endings. Use this to check progress on a long run, or to understand a failure. Returns live data while the run is still going.

Args: run_id: The run to inspect. limit: How many of the most recent activity lines to return. raw: Also return the child's full uncapped response. dsh_delegate returns a distilled version when the answer is large; this is where the original text lives.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNo
limitNo
run_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4.3/5.0
Behavior4/5

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

Without annotations, the description carries the transparency burden. It explains that it returns the tail of the activity log, live data, and the effect of the 'raw' parameter. It implies read-only behavior by saying 'Show' and 'Returns', but doesn't explicitly state it has no side effects. This is adequate given the tool's nature.

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 well-structured with a clear overview and parameter explanations. It is slightly verbose but each sentence adds value. It is not overly long relative to the complexity of the tool.

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 the tool's purpose, typical use cases, return behavior, and parameter semantics. It doesn't explain output schema (not required), but it provides enough context for an agent to decide when and how to use it. The mention of dsh_delegate relationship adds completeness.

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

Parameters4/5

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

The description explains each parameter beyond the schema: run_id (which run), limit (how many recent lines), and raw (full uncapped response). It also connects raw to dsh_delegate, adding context. Since schema coverage is 100%, the baseline is 3, but the extra semantic detail raises it to 4.

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 tool's function: 'Show what a subagent did during a run.' It specifies the content returned (tool calls, assistant messages, turn endings) and distinguishes it as a transcript tool for inspecting activity.

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 provides clear usage scenarios: 'Use this to check progress on a long run, or to understand a failure.' It also mentions live data during a run, but does not explicitly contrast with sibling tools like dsh_list or dsh_delegate, which would make the guidance stronger.

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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