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midasflow-mcp-quickstart

by io.github.midasflowai-lab

Get liquidation heatmap (price-magnet zones)

get_heatmap
Read-onlyIdempotent

Liquidation-heatmap matrix for a symbol — price levels where leveraged positions cluster and are likely force-liquidated (price-magnet zones / liquidity pools). Anticipate where a move may accelerate or stall. Market DATA, not advice; a symbol with no clustering returns an empty matrix (normal, not an error). Routes: /v1/heatmap/{symbol}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesPerp symbol, e.g. 'BTCUSDT' (case/space-insensitive).

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds value by explaining that the tool returns data (not advice) and that an empty matrix is a normal case. Does not contradict 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?

Extremely concise: two sentences plus a route note. No wasted words, front-loaded with purpose. Every sentence earns its place.

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 has only one parameter, full schema coverage, and an output schema, the description is adequate. It explains the concept, normal empty case, and route. Could mention rate limits or pagination but not necessary for this simple tool.

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?

Schema description coverage is 100% for the single parameter (symbol) with a clear example. The description does not add additional meaning beyond the schema, so baseline 3 is appropriate.

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?

Clearly states it provides a liquidation-heatmap matrix for a symbol, explains what it shows (price levels where leveraged positions cluster and may be force-liquidated), and its utility (anticipating acceleration or stalling). Distinguishes from sibling tools like get_candles or get_orderbook by being specific to heatmaps.

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?

Provides context for use ('Anticipate where a move may accelerate or stall') and clarifies that it is market data, not advice. Notes that an empty matrix is normal. Lacks explicit mention of when not to use or alternative tools, but the context is sufficient for an agent to infer usage.

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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