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yaotsakpo

ledgerfc-mcp

by yaotsakpo

get_value_bets

Retrieve upcoming value bets with positive model-versus-market edge. Requires a Pro key via Bearer header or api_key.

Instructions

PRO: upcoming picks with positive model-vs-market edge. Needs a Pro key, via an Authorization: Bearer header (preferred) or the api_key argument.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyNoOptional Pro API key (get one with /key in the Ledger FC Telegram bot). Preferred instead: send it as an Authorization: Bearer header, so it never enters the conversation.
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It explicitly states the need for a Pro key and the preferred authentication method (Bearer header) to avoid exposing the key in conversation—useful security context beyond the schema. It also implies the tool requires authorization and may fail without it, though it doesn't detail error behavior. This is strong behavioral transparency for a simple tool.

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 no wasted words. It front-loads the core purpose (PRO value bets) and then the authentication requirement. Every sentence earns its place: the first defines the tool, the second specifies the key handling. Excellent structure and conciseness.

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?

There is no output schema, so the description should convey what the tool returns. It does say 'upcoming picks' which gives a general idea, but it doesn't specify the format, pagination, fields, or any error conditions (e.g., missing key). For a simple one-param tool with no output schema, this is adequate but not complete—an agent might benefit from knowing the response structure or that results are limited to upcoming picks only.

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?

The input schema provides 100% coverage for the single optional api_key parameter, including its description and how to obtain it. The description adds a note about preferring the Authorization header, but that's not a schema parameter. Since the schema already explains the parameter, a baseline of 3 is appropriate; the description adds minimal extra semantic depth.

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 clear verb and resource: 'upcoming picks with positive model-vs-market edge'. It distinguishes itself from sibling tools like get_predictions or get_arbs by focusing on the value-bet aspect (edge vs market), though it doesn't name alternatives explicitly. A 4 is appropriate—clear but not fully differentiated from siblings.

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 explains authentication requirements (Pro key via Bearer header or api_key argument) but provides no guidance on when to use this tool versus alternatives. It doesn't mention exclusions, scenarios, or compare with sibling tools. Only implied context (PRO, edge) suggests usage, but explicit when/when-not guidance is absent.

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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