gapup-mcp

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

Name	Required	Description
`async`	No	If 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.
`period`	No	Look-back period. Default: 1y.
`ticker`	Yes	Yahoo Finance ticker symbol. Examples: AAPL (US stock), SAP.DE (Frankfurt), 7203.T (Tokyo), BTC-USD (Bitcoin), GC=F (gold futures), ^GSPC (S&P 500).
`metrics`	No	Subset of fields to include (informational — all fields always returned).
`adjusted`	No	Adjust close prices for dividends and splits. Default: true.
`interval`	No	Bar interval. Default: 1d (daily).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`stats`	Yes
`period`	Yes
`series`	Yes
`status`	Yes
`ticker`	Yes
`sources`	Yes
`currency`	Yes
`interval`	Yes
`data_points`	Yes
`quality_score`	Yes
`splits_detected`	No
`resolved_exchange`	No
`dividends_detected`	No

TDQS

A4.6/5.0

Behavior5/5

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

Description adds significant context beyond annotations: auto-detects crypto vs traditional assets, adjusts for dividends/splits, mentions fallback sources, keyless access, optional proxy for rate limits, and async behavior. 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?

Description is a single paragraph but packs essential information efficiently. It is front-loaded with purpose. Slightly long but every sentence adds value; could be broken into bullet points but not required.

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 output schema exists, description covers sources, use cases, rate limit handling, and ticker formats. It is comprehensive for a complex tool with 6 parameters and diverse use cases.

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 coverage is 100%, but description enriches parameter meanings: ticker examples, adjusted defaults, metrics being informational, async explanation. Adds value beyond schema especially for ticker and async.

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?

Description clearly states 'Fetch historical OHLCV price series for any ticker' with specific examples (stocks, ETFs, indices, commodities, cryptocurrencies). It distinguishes the tool's broad scope, though it does not explicitly differentiate from sibling tools. However, the specificity and completeness of purpose earn a top score.

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?

Description lists explicit use cases ('backtesting, factor analysis, performance attribution, charting, financial modelling') and mentions auto-detection of ticker types. It does not provide when-not-to-use guidance, but the context is clear enough for most scenarios.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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