ledgerfc-mcp

submit_prediction

Submit home, draw, and away probabilities for a match to receive CLV-based scoring.

Instructions

Submit a prediction to be scored by CLV (shadow/points).

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": {
      "contributor_id": "<contributor_id>",
      "match_id": "<match_id>",
      "p_home": "<p_home>",
      "p_draw": "<p_draw>",
      "p_away": "<p_away>"
    },
    "name": "submit_prediction"
  }
}

Input Schema

Name	Required	Description	Default
`p_away`	Yes
`p_draw`	Yes
`p_home`	Yes
`match_id`	Yes
`contributor_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 24, 2026

TDQS

C2.4/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It only mentions that predictions are scored by CLV (shadow/points) but does not state side effects, such as whether submissions overwrite existing predictions, authentication requirements, rate limits, or failure modes. This is a significant gap for a write operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no fluff, so it is concise. However, it is under-specified; being short is not the same as being well-structured since it omits critical details. It could be improved by front-loading key information in a structured format, but for sheer brevity it is acceptable.

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

Completeness1/5

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

For a tool with five required parameters, no output schema, and no annotations, the description is drastically incomplete. It does not explain the expected format of probabilities, what the response will look like, error conditions, or any prerequisites. An agent would struggle to call this tool correctly without additional context.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no explanation of the five required parameters (contributor_id, match_id, p_home, p_draw, p_away). The parameter names hint at their meaning (e.g., probabilities), but the description does not clarify units, ranges, or relationships, leaving the agent to guess.

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 states a specific verb ('submit') and resource ('prediction'), and clarifies that it is for shadow/points scoring, which distinguishes it from the sibling 'place_bet'. However, it does not explicitly name any sibling tool, relying on the 'shadow/points' hint for differentiation.

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

Usage Guidelines2/5

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

The description provides no explicit guidance on when to use this tool over alternatives. The phrase 'shadow/points' implies it is for non-real bets, contrasting with 'place_bet', but this is left implicit rather than stated as a usage rule. No exclusions or alternative routing 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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