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midasflow-mcp-quickstart

by io.github.midasflowai-lab

Score symbol (calibrated P(TP1) + plan + safety)

score_symbol
Read-onlyIdempotent

CROWN data product: calibrated P(first TP1 before SL) band + coarse trade plan (TP ladder %, SL %, weights) for ONE symbol, FOLDED with a pre-trade feed-safety check (is the feed live/real-volume/fresh, no phantom ticks). Coverage is present-or-null — most symbols return p_tp1_band=null (valid, NOT an error). Feed the band into calc_ev; never read it as buy/sell. Routes: /v1/score/{symbol} + folds /v1/symbol/check as safety.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesPerp symbol, e.g. 'BTCUSDT' (case/space-insensitive). A calibrated score exists only for recently-evaluated symbols; most return p_tp1_band=null (valid).
directionNo'long' | 'short' — steers the plan ladder only; the P(TP1) score itself is direction-agnostic.long
include_safetyNoIf true (default), also fold a /v1/symbol/check feed-safety read into the response under `safety`. Set false to skip that extra call.

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

A4.1/5.0
Behavior4/5

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

Annotations already indicate safe, read-only, idempotent behavior. The description adds further context: the result can be null for most symbols, includes a feed-safety check, and explains the folding mechanism. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph of four sentences. It is concise and front-loads the core purpose. While not structured with bullets, there is no wasted text; every sentence adds 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 complexity (3 parameters, output schema, rich annotations, multiple siblings), the description covers the main points: what it returns, null handling, usage with calc_ev, routing, and the safety fold. It is complete enough for an agent to infer correct usage.

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 baseline is 3. The description adds general context (null handling, use with calc_ev) but does not significantly enhance individual parameter semantics beyond what the schema already provides. The schema descriptions are already detailed.

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 verb (score) and resource (a calibrated P(TP1) band + trade plan + safety check for a symbol). It distinguishes from siblings like calc_ev (which consumes the band) and get_signals (different purpose). The title also reinforces the purpose.

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 explicitly tells when to use the tool (to get a score and plan) and how to use the output (feed into calc_ev). It warns against misinterpreting the band as a buy/sell signal and explains that null is valid. It does not compare with all sibling tools, but the context is sufficient.

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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