Skip to main content
Glama
elevanaltd

debate-hall-mcp

by elevanaltd

resume_debate

Resume a paused debate after an interruption or failure, picking up exactly where it stopped without losing prior arguments.

Instructions

Resume a PAUSED debate from where it left off (Phase 4).

Allows resuming debates that were paused due to failures (provider timeouts, errors, etc.) during auto-orchestration.

Args: thread_id: The thread ID of the paused debate tier: Configuration tier (default: "standard") compression_tier: Override compression tier (None = use tier default) primer_tier: Override primer tier (None = use tier default)

Returns: Dictionary with thread_id, topic, status, turn_count, and synthesis

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNostandard
thread_idYes
primer_tierNo
compression_tierNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does add useful context about resuming from Phase 4 and the failure-triggered pause scenario, plus a return summary. However, it does not disclose side effects, idempotency, whether state is mutated, or what happens if the debate is not actually paused.

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 compact, well-organized with Args/Returns sections, and front-loads the core purpose in the first sentence. Every line adds useful information without filler or redundancy.

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?

For a tool with moderate complexity, the description covers what it does, when it applies, key parameters, and return contents. The main gap is the absence of explicit preconditions or failure behavior—such as what occurs if the thread is not a paused debate—but overall it is sufficiently complete for selection and initial invocation.

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 0%, so the description must compensate. It lists each parameter and clarifies defaults and override behavior (e.g., 'None = use tier default'). However, it does not define what the tiers mean or how compression_tier and primer_tier affect behavior, leaving semantic ambiguity beyond the schema.

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 verb ('Resume') and a precise resource ('a PAUSED debate') and adds the phase detail ('from where it left off (Phase 4)'). This clearly distinguishes it from siblings like init_debate, run_debate, and get_debate.

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 states when to use the tool: resuming debates paused due to failures during auto-orchestration, such as provider timeouts or errors. It does not explicitly mention when not to use it or name alternative tools, but the context is clear enough for an agent to identify the intended scenario.

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