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Get Politician P&L

get_politician_pnl
Read-only

Estimate realized and unrealized profit and loss for a politician's stock trades using disclosed transaction data. Provides totals and per-ticker breakdown.

Instructions

Get estimated realized + unrealized profit & loss for a politician. Methodology: each disclosed trade amount range is converted to an estimated share count using the stock's historical market price on the transaction date, then FIFO-matched on SHARES (realized = (sellPrice − buyPrice) × matched shares); open positions are marked to the current price for unrealized P&L. Works for Congress (sen-/rep-) AND executive branch (exec-) officials. Response includes a totals object (estimatedRealizedPnl, estimatedUnrealizedPnl, winRate, realizedTrades, tickersTraded) and a byTicker[] breakdown (estimatedShares, avgCostBasis, currentPrice, realizedPnl, unrealizedPnl, unrealizedPnlPercent) — byTicker open positions double as the estimated holdings. All figures are ESTIMATES (±25-40% from disclosure bracket width). Use get_politicians first to find the slug.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesPolitician URL slug — congressional ("sen-nancy-pelosi", "rep-...") or executive ("exec-trump-donald-j")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
Behavior5/5

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

Discloses that figures are estimates with ±25-40% error, details the FIFO and mark-to-market methodology, and matches the readOnlyHint annotation. No contradictions.

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 somewhat long but well-structured, front-loading purpose and methodology, then explaining output and accuracy. 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, the description covers methodology, output structure, and accuracy. The output schema exists, so the description's overview is sufficient. Minor gap: no explicit mention of pagination or limits.

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 context about slug format and prerequisite, but the schema already includes a detailed description of the slug parameter.

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?

The description clearly states the tool retrieves estimated P&L for a politician, with specific resource and action. It mentions scope (Congress and executive branch) but does not explicitly differentiate from sibling tools like get_politician_activity or get_politician_transactions.

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?

Provides explicit prerequisite: 'Use get_politicians first to find the slug.' Also explains methodology and accuracy caveats, helping the agent decide when to use this tool. Lacks explicit when-not-to-use conditions.

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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