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midasflow-mcp-quickstart

by io.github.midasflowai-lab

[LAB] Calculate expected value (local analytics, no network)

calc_ev
Read-onlyIdempotent

LAB tool — pure local expected-value calculator over a win-rate and average win/loss percentages. No network, no advice, 0 units. Feed a get_accuracy bucket win-rate (or a score_symbol band) to reason about expectation per trade.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stake_usdNoNotional stake to scale EV into dollars. Default 100.
avg_win_pctYesAverage gain on a win, in percent (e.g. 2.0 = +2%).
avg_loss_pctYesAverage loss on a loss, in percent magnitude (e.g. 1.5 = -1.5%). Sign is ignored.
win_rate_pctYesProbability of a winning trade, in percent (0–100). Feed from a get_accuracy bucket win-rate.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds 'pure local', 'No network, no advice, 0 units,' which reassures agents of safety and no side effects. No contradiction.

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?

Two sentences with no redundancy: first sentence states purpose and location, second sentence adds usage guidance. Every word contributes value.

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?

Given the tool's simplicity, the description is sufficient. The presence of an output schema further reduces the need to explain return values. Context about feeding from other tools is provided.

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

Parameters4/5

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

Schema description coverage is 100%, but description adds value by advising 'Feed from a get_accuracy bucket win-rate' for win_rate_pct and noting 'Sign is ignored' for avg_loss_pct, which clarifies usage beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it is a 'pure local expected-value calculator' using win-rate and win/loss percentages, and explicitly notes 'No network, no advice, 0 units.' It distinguishes itself from sibling data-retrieval tools like get_accuracy by instructing to feed its output into this calculator.

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

Usage Guidelines4/5

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

Description provides context on when to use (reason about expectation per trade) and explicitly mentions feeding from get_accuracy or score_symbol. Does not formally list when not to use, but the use case is clear from the description.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct aspect of market data and analytics: account info, analysis, backtesting, expected value, accuracy, candles, context, flow, heatmap, market overview, orderbook, signals, whales, and scoring. Despite some thematic overlap (e.g., get_accuracy and score_symbol both involve probabilities), descriptions clearly differentiate their purposes and usage contexts.

Naming Consistency2/5

Naming is inconsistent: some tools use the 'get_' prefix (get_accuracy, get_candles, etc.), while others are bare verbs or nouns (account, analyze, backtest, calc_ev, score_symbol). This mix of patterns (get_ vs verb vs noun) makes the naming convention unpredictable.

Tool Count5/5

With 14 tools, the server is well-scoped for a comprehensive market data and analytics API. Each tool serves a clear and distinct function, and the count is neither too few to cover the domain nor too many to be overwhelming.

Completeness5/5

The tool set covers all major aspects of the domain: account management, historical data (candles), market context (regime, flow, heatmap), order book, signals, accuracy/backtesting, and scoring. There are no obvious missing operations for an analytics-focused financial data server.

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