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mcp-knowledge

by io.github.getgapup

historical_price_series

Read-onlyIdempotent

Fetch historical OHLCV price series for any ticker: stocks (AAPL, SAP.DE, 7203.T), ETFs, indices, commodities (GC=F for gold) or cryptocurrencies (BTC-USD). Returns a full date-indexed series of open/high/low/close/volume plus pre-computed statistics: total return, annualised return (CAGR), annualised volatility, max drawdown and Sharpe estimate (rf=4%). Automatically detects crypto tickers (→ CoinGecko) vs traditional assets (→ Yahoo Finance primary, Stooq fallback). Adjusts for dividends and splits when adjusted=true (default). Use cases: backtesting, factor analysis, performance attribution, charting, financial modelling. Sources: Yahoo Finance, CoinGecko, Stooq. All keyless. Optional env: AICI_RESEARCH_PROXY_URL for Bright Data routing (lifts Yahoo 429), TWELVE_DATA_API_KEY for higher Twelve Data quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
periodNoLook-back period. Default: 1y.
tickerYesYahoo Finance ticker symbol. Examples: AAPL (US stock), SAP.DE (Frankfurt), 7203.T (Tokyo), BTC-USD (Bitcoin), GC=F (gold futures), ^GSPC (S&P 500).
metricsNoSubset of fields to include (informational — all fields always returned).
adjustedNoAdjust close prices for dividends and splits. Default: true.
intervalNoBar interval. Default: 1d (daily).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statsYes
periodYes
seriesYes
statusYes
tickerYes
sourcesYes
currencyYes
intervalYes
data_pointsYes
quality_scoreYes
splits_detectedNo
resolved_exchangeNo
dividends_detectedNo

TDQS

A4.5/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: auto-detection of crypto vs. traditional sources, adjustment for dividends/splits, keyless access, and optional environment variables for rate limits. Annotations provide readOnlyHint and idempotentHint, and the description reinforces these while adding rich 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 (a few sentences) and front-loaded with the core action and asset types. Each sentence adds value: returns, statistics, source detection, adjustment, use cases, sources, and access details. No filler or 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's complexity (6 parameters, 100% schema coverage, output schema exists, annotations), the description covers asset types, return format, statistics, source detection, adjustment, use cases, and access limitations. It is comprehensive enough for an agent to select and invoke correctly without needing to infer missing details.

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 each parameter is already described in the input schema. The tool description adds overall context (e.g., auto-detection of ticker source) but does not significantly enhance parameter-level understanding beyond what the schema provides. Baseline score of 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?

The description clearly states 'Fetch historical OHLCV price series for any ticker' with specific examples of stocks, ETFs, indices, commodities, and cryptocurrencies. It distinguishes itself from sibling tools by being a generic historical price fetcher for any ticker, while siblings like fx_rate or economic_indicator cover specific data types.

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 lists explicit use cases such as backtesting, factor analysis, and charting, providing context for when to use the tool. However, it does not explicitly state when not to use it or directly compare with alternatives like fx_rate or china_market_data, which would improve clarity.

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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