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NightWatch Live Intelligence

by io.github.jamboree777

get_token_intel

ONE-call token intelligence for trading decisions: rating (grade, 15-step grade_detail, GPA, outlook, verdict, lifecycle), per-venue grades, unanimous-club membership, microburst quality score, warning severity/convergence, prediction hit-rate + history, KG fill, 1d cross-exchange spread, open/blocked transfer routes, PLUS coverage (Coverage Grade v0: A-D score of how well NightWatch observes this asset, with per-layer L1-L8 booleans) and bti (Bot-Tradability Index v0: per-strategy verdicts ok/limited/no/unknown with reasons and limit_usd for arbitrage, momentum_swing, market_making, lending_short, plus overall t_grade). Call this FIRST before deciding what to do with a token.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesTrading pair symbol (e.g. BTC/USDT)
exchangeYesExchange ID (e.g. binance, upbit, gateio)

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It focuses on output data but does not mention side effects, authentication requirements, rate limits, or whether it is read-only. The 'ONE-call' hint implies a single request but lacks detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is overly verbose, listing many data fields in a single paragraph. It lacks structure (e.g., bullet points) and could be shortened while retaining essentials. The front-loaded purpose is good, but the length impacts clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (many data fields) and no output schema, the description is fairly comprehensive in enumerating outputs. However, it does not explain how to interpret or use the data for trading decisions, leaving gaps.

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 description coverage is 100%, so baseline is 3. The description does not add extra meaning to the parameters beyond what the schema provides. It lists output data but does not elaborate on parameter usage.

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 states this is a 'ONE-call token intelligence for trading decisions' and lists specific data fields. It distinguishes from siblings by explicitly advising 'Call this FIRST before deciding what to do with a token.' The verb 'get' and resource 'token intel' are clear.

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 explicitly guides when to use the tool: 'Call this FIRST before deciding what to do with a token.' It provides clear context but does not explicitly state when not to use it or list alternatives.

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.

Resources