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midasflow-mcp-quickstart

by io.github.midasflowai-lab

Get accuracy / outcomes (proof of edge) [grouped]

get_accuracy
Read-onlyIdempotent

Realized track-record (proof of edge, forward-only, Wilson-bounded), grouped by kind. kind='accuracy' (default)=source×boost first-TP win-rate + boost lift in the canonical TP1 frame (/v1/accuracy); kind='outcomes'=realized aggregate track-record over a window from resolved signal_outcomes, model-free (/v1/outcomes). Win-rate frame = P(price hit TP1 before SL), NOT realized PnL. Model internals never exposed. Empty/unknown kind → a menu of kinds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo'accuracy' (default, /v1/accuracy) | 'outcomes' (/v1/outcomes). Empty/unknown → menu.accuracy
sourceNoSource-family filter (kind='accuracy', client-side): '' = all | 'pump' | 'flow' | 'breakout' | 'unified' | 'other'.
windowNoTrack-record window in days (kind='outcomes'; passed through to /v1/outcomes). 0 = endpoint default.

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

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds significant context: win-rate frame explained (P(price hit TP1 before SL, not realized PnL)), model internals never exposed, and forward-only Wilson-bounded metrics. This provides transparency beyond annotations.

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 concise, well-structured, and front-loaded. Every sentence provides distinct information without redundancy. It efficiently explains both modes and key caveats.

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 presence of an output schema, the description covers the essential behavioral context (two kinds, win-rate definition). It does not address performance or pagination but is sufficient for agent selection. Slight gap in not mentioning rate limits or error handling.

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 coverage is 100%, providing baseline. The description adds value by explaining the meaning of each 'kind' (accuracy vs outcomes) and the behavior for empty/unknown kind (returns a menu). This enriches the schema descriptions.

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 retrieves realized track-record (proof of edge) grouped by kind, and distinguishes between 'accuracy' and 'outcomes' modes. However, it does not explicitly differentiate from sibling tools like 'calc_ev' or 'get_signals', which have overlapping purposes.

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 does not provide guidance on when to use this tool versus alternatives. It explains the two modes but lacks explicit context for selection among sibling tools, such as when to prefer 'get_accuracy' over 'calc_ev' or 'get_signals'. No exclusions or prerequisites are mentioned.

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.

Resources