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Wallet P&L

wallet_pnl
Read-only

Any wallet's profit and loss on Base, from public chain data. Wallet: its stock tokens since 1 Oct 2026, average cost from the wallet's own USDC trades on any venue (DEX or orderbook), realised and unrealised per token; transfers are never priced. Strategies: one row per strategy (live and cancelled orders it placed, incl. its RiverAccount): fills, USDC spent and received, tokens bought and sold, and P&L at today's prices (value now minus what was put in). Robinhood Chain is not counted yet (stated in notes).

Input Schema

NameRequiredDescriptionDefault
addressYes0x... wallet address

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior5/5

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

Annotations already signal readOnlyHint=true/openWorldHint=false, and the description substantially exceeds that bar with genuine behavioral detail: cost basis is derived from the wallet's own USDC trades on any venue, transfers are explicitly never priced, coverage of 'live and cancelled orders' is stated, and the P&L formula (value now minus what was put in) is spelled out. These are exactly the nuances an agent needs to interpret results correctly.

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 core purpose is front-loaded in the first clause, and the dense remainder is information-dense rather than padded — each clause adds a rule (pricing method, exclusions, strategy row contents). The heavy semicolon structure makes it harder to parse than ideal, but nothing is wasted.

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?

With no output schema, the description correctly carries the return-shape burden, defining both the per-token and per-strategy row contents. Coverage limitations are disclosed. The only friction is the oddly specific and possibly erroneous 'since 1 Oct 2026' cutoff, which invites confusion about the accounting window.

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?

Only one parameter (address) and schema description coverage is 100%, so the schema fully documents the input. The description adds no syntax or format guidance for the address beyond what the schema already provides, which is the baseline-3 case.

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?

States a specific verb+resource: profit and loss for any wallet on Base, computed from public chain data, with per-token and per-strategy breakdowns. Scope limits (Base only, Robinhood Chain excluded, transfers never priced) make the deliverable unambiguous. It does not, however, distinguish itself from the sibling wallet_holdings, which an agent must choose between.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the specificity of the output ('realised and unrealised per token', 'one row per strategy'), so an agent can infer when P&L is the right request. But there is no explicit when-to-use, when-not-to-use, or named alternative (e.g. wallet_holdings for raw positions), so the routing decision is left to inference.

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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