Roundtable MCP

route_prompt

Route a prompt to a specific expert model or auto-detect the best target based on trigger keywords for multi-model orchestration.

Instructions

Routes prompt to a single specific expert model or auto-detects target based on prompt triggers.

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": {
      "prompt": "<prompt>"
    },
    "name": "route_prompt"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes
`target_model_id`	No	ID of the target model (e.g., 'gemma-local', 'claude-sonnet', 'deepseek-r1').

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 18, 2026

TDQS

A3.5/5.0

Behavior2/5

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

With no annotations, the description must fully disclose behavioral traits. It only states the core action and auto-detection, but omits details like what happens when both parameters are provided, error handling, authentication requirements, or how auto-detection triggers work.

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?

A single, front-loaded sentence of 16 words that conveys the essential function without any redundancy. Every word is necessary.

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

Completeness3/5

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

For a simple tool with two parameters and no output schema, the description covers the basic purpose but leaves gaps around auto-detection behavior, error scenarios, and parameter interaction. It is minimally adequate but not fully complete.

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 50% (prompt missing description). The description adds context that 'prompt' is the input to be routed, which is helpful but does not specify format or constraints. For 'target_model_id', the schema already provides a clear description with examples, so the description adds no extra value. Baseline 3 is appropriate given borderline coverage.

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 verb ('routes prompt') and resource ('specific expert model'), distinguishing it from siblings like 'run_roundtable' which likely sends to multiple models. The mention of 'auto-detects' further clarifies the tool's dual mode.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly suggests when to use this tool (single model routing vs. auto-detection) but does not explicitly contrast with alternatives like 'run_roundtable' or state when not to use it. No exclusions or prerequisites are provided.

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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