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midasflow-mcp-quickstart

by io.github.midasflowai-lab

[LAB] Analyze symbol (AI-Analyst report — heaviest)

analyze
Read-onlyIdempotent

LAB / RESEARCH tool — the full MidasFlow AI-Analyst report for a symbol: a synthesized narrative fusing flow, derivatives, levels, regime and signal context into a human-readable read RIGHT NOW. This is the HEAVIEST call (composes many sub-feeds) so it costs MORE units and requires a TOP tier; call it sparingly, AFTER cheaper context tools. Market DATA / AI-generated analytics, NOT financial advice. Routes: /v1/analyze/{symbol}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoReport language, e.g. 'en' | 'ru' | 'uk' | 'es' (BCP-47-ish). Default 'en'.en
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

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it composes many sub-feeds, is the heaviest call, and costs more units, offering valuable behavioral context beyond the 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 extremely concise, using bold for emphasis, and front-loads critical information. Every sentence adds value, with no repetition or filler.

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 complexity of the tool (heavy AI-Analyst report with multiple sub-feeds), the description covers purpose, usage order, cost tier, and disclaimer. An output schema exists, so return values need not be explained. The description is fully sufficient for selecting and invoking the tool correctly.

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 coverage is 100%, so the schema already documents both parameters. The description does not elaborate on parameter details, but it implies the symbol is the main input. A baseline score of 3 is appropriate as the description adds minimal value beyond the 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?

The description clearly identifies the tool as the full AI-Analyst report for a symbol, synthesizing multiple data sources into a human-readable narrative. It distinguishes itself from sibling tools by calling itself the 'heaviest' call, which is unique among the listed siblings.

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

Usage Guidelines5/5

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

Explicitly advises to use sparingly, after cheaper context tools, and notes higher cost and top-tier requirement. It also cautions that output is not financial advice, providing clear usage boundaries.

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