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midasflow-mcp-quickstart

by io.github.midasflowai-lab

Get order-flow (cvd / sweeps / cross / vpin) [grouped]

get_flow
Read-onlyIdempotent

Per-symbol order-flow microstructure, grouped by kind. kind='vpin'/'all' (default)=full snapshot (CVD trend+divergence, VPIN toxicity, sweep state, buy/whale aggression, /v1/flow); 'cvd'=CVD series (/v1/cvd); 'sweeps'=stop-sweep events (/v1/sweeps); 'cross'=cross-exchange flow origin (/v1/cross_flow). Realtime market CONTEXT, NOT advice / a win-rate. Empty/unknown kind → a menu of kinds. VPIN is ONE contested signal — pair with get_accuracy before treating it as edge.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo'vpin'/'all' (full /v1/flow snapshot, default) | 'cvd' (/v1/cvd series) | 'sweeps' (/v1/sweeps) | 'cross' (/v1/cross_flow origin). Empty/unknown → menu.all
symbolNoPerp symbol, e.g. 'BTCUSDT' (case/space-insensitive). Resolves the cross-venue aggregate.

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

A4.5/5.0
Behavior4/5

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

The description discloses behavioral traits beyond annotations: it notes the tool is real-time context (not advice), explains the behavior for empty/unknown kind (returns a menu), and warns that VPIN is a contested signal. Annotations already confirm read-only, idempotent, non-destructive nature, so the description adds complementary detail.

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 and front-loaded. It uses two sentences and a bullet-like listing of kinds with parenthetical clarifications. Every sentence adds value with no redundancy. The structure is efficient for an AI to parse quickly.

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

Completeness5/5

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

Given the tool has only two parameters with complete schema descriptions, a rich set of annotations, and an output schema, the description covers all necessary aspects: purpose, parameter variants, usage caveats, and behavioral notes. It is fully adequate for an agent to select and invoke this tool correctly.

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?

The input schema already has full coverage (100%) with descriptions for both parameters. The description adds extra meaning by elaborating on each kind's behavior (e.g., 'cvd' returns CVD series) and the menu behavior for empty/unknown kind, providing richer context than the schema alone.

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?

The description clearly states the tool retrieves per-symbol order-flow microstructure grouped by kind. It explicitly lists the available kinds (vpin, cvd, sweeps, cross) and what each returns, and distinguishes itself from sibling tools like get_accuracy by noting VPIN is contested and should be paired with get_accuracy.

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?

The description provides clear usage context: it returns real-time market context, not advice. It advises pairing VPIN with get_accuracy for edge verification. However, it does not explicitly state when not to use this tool or when alternatives like get_signals or get_whales might be more appropriate.

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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