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mcp-knowledge

by io.github.getgapup

realtime_data_streams

Read-only

High-frequency real-time market data for trading agents, market-making bots and fintech analysts. Returns FX ticks (bid/ask/spread), intraday OHLCV candles, crypto orderbook snapshots (depth 5-50), recent trades with VWAP, and sovereign bond yields. All sources are keyless public REST APIs (Binance, Coinbase, Kraken, OKX, open FX feeds, worldgovernmentbonds.com). Ultra-short cache: 10s for ticks/trades, 60s for orderbook. Use when an agent needs live market data as precise numeric inputs for trading logic, arbitrage detection, or portfolio valuation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesData stream type: fx_tick (latest FX bid/ask/mid/spread), fx_history_intraday (OHLCV candles), crypto_orderbook (order book snapshot), crypto_trades_recent (last 50 trades + VWAP), bond_yields (sovereign yield %)
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
depthNoOrderbook depth (levels each side) for crypto_orderbook mode (default: 20)
periodNoCandle period for fx_history_intraday mode (default: 5m)
symbolYesMarket symbol. FX: EURUSD, GBPUSD, USDJPY. Crypto: BTCUSDT, ETHUSDT, BTC-USD. Bonds: US10Y, US2Y, DE10Y, FR10Y, UK10Y, JP10Y, IT10Y

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
symbolYes
fx_tickNo
sourcesYes
fx_historyNo
bond_yieldsNo
crypto_tradesNo
quality_scoreYes
crypto_orderbookNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds valuable behavioral details: ultra-short cache durations (10s for ticks/trades, 60s for orderbook), keyless public REST APIs, and async capability. No contradiction 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.

Conciseness5/5

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

Extremely concise: two sentences that front-load purpose and audience, then list data types, sources, cache info, and usage recommendation. Every sentence adds value with no redundancy.

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 5 parameters, output schema, and annotations, the description covers all essential aspects: data types, sources, cache, async behavior, and usage context. Output schema exists, so return structure is not needed in description. Complete for a real-time data tool.

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 description coverage is 100%, so baseline is 3. Description enhances parameter understanding by providing concrete symbol examples and explaining the async parameter use for slow tools. Adds context beyond 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?

Clearly states the tool provides high-frequency real-time market data for specific use cases like trading and arbitrage. Lists multiple data types and sources, but does not explicitly differentiate from sibling tools like fx_rate or historical_price_series which also provide market data.

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?

Explicitly states when to use: 'Use when an agent needs live market data as precise numeric inputs for trading logic, arbitrage detection, or portfolio valuation.' Also provides context on sources and cache durations, but lacks explicit when-not-to-use or alternative tools.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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