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NightWatch Live Intelligence

by io.github.jamboree777

get_quartermaster

Quartermaster (Thusus shadow-fund capital allocator) DECISION STATE from its last pass: per-venue working-capital floor/cap bands (floor = max(2x avg 7d trade size, p75 daily buy volume); cap = 3x floor) with under/band/over status, concentration flags, hedge reservations, planned paper rebalance moves, the USD/KRW FX snapshot used, and logistics accounting (transfer costs, arb-as-rebalance gains). SHADOW MODE: paper fund — planned moves are paper records, no real capital moves. Outcomes (live balances / executed transfer log) are separate: see the /arb/thusus/fund and /arb/thusus/transfers endpoints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

No annotations exist, so the description carries full burden. It discloses shadow mode, paper-only moves, and the absence of real capital actions. It does not explicitly state read-only, but the context implies safety.

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 despite the detailed field list, with every sentence adding necessary context. It front-loads the core purpose and avoids 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 no output schema, the description fully enumerates the returned data (floor/cap bands, status, flags, FX, logistics). It also provides operational context (shadow mode) and pointers to related endpoints, making it self-contained.

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?

No parameters are defined; schema coverage is 100% by absence. Description adds value by explaining what the single implied input (the fund state) contains, meeting the baseline for 0 parameters.

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 tool returns the 'DECISION STATE' of the Quartermaster shadow fund, listing all included fields. It distinguishes from separate live-balance endpoints, making the tool's exact scope obvious.

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?

While it references separate endpoints for live outcomes (guiding away from misuse), no explicit when-to-use vs. siblings is given. Sibling tools are not mentioned, but the contrast with other endpoints is helpful.

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

A3.8/5.0
Disambiguation3/5

Most tools have distinct purposes, but get_token_intel, get_token_research, and get_microburst overlap in coverage of token intelligence, which could cause agent misselection. Descriptions are detailed but some redundancy exists.

Naming Consistency4/5

Tools follow a consistent verb_noun pattern (agent_*, get_*, search_tokens). Minor deviations like 'get_microburst' and 'get_quartermaster' use less conventional nouns, but overall pattern is clear.

Tool Count5/5

With 15 tools, the count is well-scoped for an intelligence platform covering agent interaction, token data, trading insights, and cross-venue analysis. Each tool serves a distinct purpose without being overwhelming.

Completeness4/5

The set covers identity management, fundamental data, price/market stats, orderbook microstructure, cross-venue verification, and comprehensive token intelligence. Minor gaps like historical data or advanced analytics are omitted, but core workflows are well-supported.

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