v12-dfs

betting_my_bets

My Bets

A user's bet history + P&L / CLV summary. avg_clv_pct > 0 over time = you're beating the closing line = real edge (the only durable proof).

Responses:

200: Successful Response (Success Response) Content-Type: application/json 422: Validation Error Content-Type: application/json

Example Response:

{
  "detail": [
    {
      "loc": [],
      "msg": "Message",
      "type": "Error Type",
      "ctx": {}
    }
  ]
}

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": {
      "uid": "<uid>"
    },
    "name": "betting_my_bets"
  }
}

Input Schema

Name	Required	Description	Default
`uid`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedJun 17, 2026

TDQS

C2.7/5.0

Behavior3/5

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

No annotations provided, so description must disclose behavioral traits. It explains that the tool returns a summary of bets with P&L and CLV, and adds an interpretive note about avg_clv_pct. However, it does not explicitly state that it is a read-only operation, nor does it discuss authentication, rate limits, or side effects. The response code section is boilerplate and does not add behavioral context.

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?

The core description is concise and front-loaded with the purpose. The inclusion of response code examples and a large error response JSON adds some clutter that could be omitted as standard boilerplate, but the overall structure is clear and efficient.

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

Completeness2/5

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

Given the lack of an output schema, the description should fully explain what the tool returns. It mentions 'bet history + P&L / CLV summary' but does not detail the fields, structure, or pagination. The error example provides no insight into the success response. The tool is therefore incomplete for understanding the exact output.

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?

The input schema has one required parameter 'uid' with no description, and the tool description does not mention it at all. Schema description coverage is 0%, and the description fails to add any meaning about what 'uid' represents (likely user ID) or its constraints.

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 clearly states that the tool returns a user's bet history with P&L and CLV summary, and provides interpretive context about avg_clv_pct indicating edge. It effectively distinguishes this from other betting tools like betting_log_bet (write operation) and betting_analyze_prop (analysis). However, it could be more precise about what data is returned (e.g., fields, format).

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?

No explicit guidance on when to use this tool versus alternatives. The description implies it's for checking a user's own betting history and performance, but does not mention when not to use it or suggest other tools for specific needs (e.g., for market lines or sharp ratings).

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

All v12-dfs tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?