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Dagon Inference

Dagon Inference

Official
by inkwell-finance
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Chat with a Dagon model

dagon_chat

Send a prompt or full conversation to a Dagon model to get a non-streaming chat completion reply; each call uses credits.

Instructions

Send a chat completion to a Dagon model (POST /v1/chat/completions, non-streaming) and return the reply. Spends credits.

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": {
      "model": "<model>"
    },
    "name": "dagon_chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYesModel id from dagon_models.
promptNoUser message. Use this or messages.
systemNoOptional system prompt, prepended to the conversation.
api_keyNoDagon account key (gk_...). Defaults to the DAGON_API_KEY environment variable.
messagesNoFull conversation, OpenAI chat format. Use this or prompt.
max_tokensNo
temperatureNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false and openWorldHint=true, so the safety profile is partly covered; the description adds meaningful context by disclosing the HTTP endpoint, that responses are non-streaming, and that the call consumes credits. It does not say what happens on insufficient credits, whether the cost is per-token, or how errors surface, which matters for a spending operation.

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?

Two tight sentences, front-loaded with the action and followed by the cost warning. Nothing is wasted, though the parenthetical endpoint is of marginal use to an agent compared to a note on the reply shape.

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 7-parameter, non-read-only tool with no output schema, the definition covers the action, transport mode, and cost but omits the return shape (e.g. where the reply text lives) and failure modes. The cost disclosure is the strongest completeness element; the rest is adequate but thin.

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 71%, so most parameters are documented in the schema and the baseline of 3 applies. The description adds no parameter-level meaning at all — notably max_tokens and temperature are undocumented in both places, and the prompt/messages mutual exclusivity is only handled by the schema.

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 names a specific verb and resource ('send a chat completion to a Dagon model'), adds the underlying endpoint and the non-streaming constraint, and states that it returns the reply. It is clearly distinct from balance/key/credit siblings, though it does not explicitly contrast with dagon_models, which it depends on for model ids.

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?

Usage is only implied: this is the tool for getting a model response, and the closing 'Spends credits' is a useful cost caution. There is no explicit when-to-use/when-not statement, no mention of how to obtain a model id beyond the schema's reference to dagon_models, and no guidance on prompt vs messages 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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