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pairbook-io

pairbook-mcp

Official
by pairbook-io

Compare two assets

compare_pair
Read-onlyIdempotent

Compare any two US stocks or ETFs for correlation, beta, volatility, max drawdown, fund facts, and holdings overlap. ~4,700 symbols; unknown tickers require resolve_symbol first.

Instructions

Correlation (weekly returns), annualized covariance, beta vs S&P 500, volatility, max drawdown, calendar-year returns, fund facts (fees, yield, AUM) or stock fundamentals (market cap, P/E), and ETF holdings overlap with top common positions, for a pair of US stocks or ETFs. Popular pairs come from PairBook's precomputed matrix; any other pair of covered symbols is computed on demand from the weekly return series. Example: a=QQQ, b=VOO. Symbol order does not matter. Covers ~4,700 US-listed stocks and ETFs only (no crypto, no foreign listings, no inverse/leveraged short funds, no intraday data); figures use weekly closes and refresh once per trading day. Unknown tickers return an error: resolve them with resolve_symbol first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aYesFirst ticker, e.g. AAPL or SPY
bYesSecond ticker, e.g. MSFT or VOO

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
aYes
bYes
docsNo
fundNo
noteNo
pageNo
as_ofYes
termsNo
fundamentalsNo
beta_vs_spy_3yNo
holdings_overlapNo
computed_on_demandNo
correlation_weeklyYes
max_drawdown_3y_pctNo
volatility_ann_pct_3yNo
covariance_ann_pct2_3yNo
calendar_year_returns_pctNo
Behavior5/5

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

Beyond the readOnlyHint/idempotentHint/destructiveHint annotations, the description reveals important behaviors: popular pairs are served from a precomputed matrix, any other pair is computed on demand from weekly returns, data refreshes once per trading day, and unknown tickers produce an error. These are precisely the behavioral traits an agent needs to set expectations.

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 dense but every sentence earns its place: output list, coverage universe, computation mode, example, commutativity, limitations, refresh cadence, and error handling. The most important purpose is front-loaded, and details are logically organized rather than padded.

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?

For a pairwise comparison tool with an output schema and safety annotations, this description is complete: it defines the symbol universe, data frequency, computation path, return value caveats, and failure mode. An agent can determine correct usage and expected behavior without needing supplemental information.

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?

The schema already documents both parameters at 100% coverage, so the baseline is 3. The description adds material value by explicitly stating 'Symbol order does not matter' and providing a concrete example (a=QQQ, b=VOO), which removes a likely ambiguity about whether parameter order is significant.

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 specifies the verb 'Compare' with a resource ('a pair of US stocks or ETFs') and enumerates the exact outputs computed, from correlation and beta to holdings overlap. This clearly differentiates it from siblings like symbol_profile and analyze_portfolio, which are single-asset or multi-asset portfolio tools.

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 gives strong usage context: it covers US-listed equities/ETFs only, excludes crypto, foreign listings, inverse/leveraged funds, and intraday data, and explicitly instructs agents to call resolve_symbol first for unknown tickers. It stops short of naming sibling alternatives such as find_diversifiers or analyze_portfolio for when a single comparison is not the right fit.

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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