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moa-mcp

moa-mcp

by cvanes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

execute_prompt

Send a single-turn prompt to any available model from providers like OpenAI, Ollama, or OpenRouter and retrieve its response. Optionally attach local text or image files for context.

Instructions

Execute a single-turn prompt against a model and return its response.

provider and model_id must match an entry from list_models. attachments
is an optional list of local file paths (text or images) included as
context with the prompt.

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": {
      "provider": "<provider>",
      "model_id": "<model_id>",
      "prompt": "<prompt>"
    },
    "name": "execute_prompt"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYes
model_idYes
providerYes
attachmentsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the interaction is single-turn and that attachments are local file paths, which is useful. However, it doesn't mention any side effects, error conditions, authentication requirements, or rate limits that might affect an agent's decision-making.

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 two sentences with a clear front-loaded purpose, followed by essential parameter context. Every word earns its place, with no filler or repetition of schema information.

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?

Given an output schema exists, return values are already covered. The description provides key context about the prerequisite relationship with list_models and the nature of attachments. It could be more complete by mentioning error handling or authentication, but overall it is sufficient for an agent to use the tool correctly.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful context by stating that provider and model_id must correspond to entries from list_models and that attachments are local file paths. The 'prompt' parameter is not explicitly described but is self-explanatory, so this is a strong compensation for the lack of schema descriptions.

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 executes a single-turn prompt against a model and returns its response. The verb 'execute' and resource 'prompt' are specific, and it clearly distinguishes from the sibling tool list_models.

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?

It explicitly mentions that provider and model_id must match an entry from list_models, giving clear context on prerequisites and implicitly guiding the agent to call list_models first. It also notes attachments are optional, but doesn't explicitly state when to use this tool over others, so it loses a point.

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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