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Glama

ledgerfc

by io.github.yaotsakpo

get_arbs

PRO: recently detected live arbitrage opportunities. 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.
live_rescanNoIf true, fetch fresh odds NOW and return current arbs (age ~0) instead of logged ones. Rate-limited (costs API credits). Falls back to logged arbs if unavailable.
within_secondsNoLook-back window in seconds. Default is tight because arbs die fast; widen only for a historical view.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / live_rescan
      Added value: +{
+  "default": false,
+  "description": "If true, fetch fresh odds NOW and return current arbs (age ~0) instead of logged ones. Rate-limited (costs API credits). Falls back to logged arbs if unavailable.",
+  "type": "boolean"
+}
  2. Changed4 schema fields changed
    • changedInput schema / properties / api_key / description
      Previous value: -"Pro API key (get one with /key in the Ledger FC Telegram bot)."New value: +"Optional 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."
    • changedInput schema / properties / within_seconds / default
      Previous value: -3600New value: +180
    • changedInput schema / properties / within_seconds / description
      Previous value: -"Look-back window in seconds."New value: +"Look-back window in seconds. Default is tight because arbs die fast; widen only for a historical view."
    • changedInput schema / required
      Previous value: -[
-  "api_key"
-]New value: +[]
  3. First observed

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations available, the description takes on the burden of behavioral transparency. It clearly discloses the requirement for a Pro key, the preferred authentication method, and the fallback to the api_key argument. It also notes that live recanning has rate limit implications in the parameter schema. It stops short of describing failure modes or response shape, but covers the most important operational constraints.

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 concise sentences that immediately state the purpose and the key prerequisite. There is no filler, and the most important information is front-loaded.

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?

For a getter with zero required parameters and detailed schema descriptions, the description provides enough context and authentication. It lacks mention of response format, and doesn't explicitly describe failure/unauthorized behavior, but those are minor and the overall context is sufficient for practical use.

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 schema description coverage is 100%, with each parameter clearly documented. The description mentions the api_key argument but adds no meaningful detail beyond the schema. Baseline of 3 is appropriate.

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?

Clearly identifies the tool as returning live arbitrage opportunities that were recently detected, and the name 'get_arbs' reinforces the action. It does not explicitly use a verb like 'get' or 'retrieve', but the purpose is recognizable from the phrase and context. It is distinguishable from sibling tools like get_value_bets and get_predictions, though it doesn't explicitly state those differences.

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?

The description implies it is used by Pro users with a valid key, and explains how to authenticate, but gives no explicit guidance on when to choose this tool over siblings or when not to use it. The usage context is implied rather than clearly stated.

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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TDQS

B3.2/5.0
Disambiguation4/5

Most tools target clearly distinct resources (predictions, bets, balance, leaderboard, record). However, get_predictions and get_value_bets are quite similar in scope—one being the unfiltered version of the other—so an agent could misselect without clearly reading paywalled requirements.

Naming Consistency4/5

The get_ prefix is used for most retrieval operations, with place_bet, submit_prediction, fetch, and search as notable exceptions. The verb-noun structure is otherwise consistent, so the deviations are minor.

Tool Count5/5

The 13-tool surface is proportional to the server's purpose of enabling paper betting, prediction retrieval, and performance tracking. Each tool maps to a distinct action within those workflows without feeling redundant.

Completeness5/5

The domain covers the full lifecycle: searching/fetching predictions, submitting and valuing predictions, placing and viewing bets, checking balance, and viewing performance via track record, CLV scores, and leaderboard. No obvious gaps prevent core workflows.

Resources