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NightWatch Live Intelligence

by io.github.jamboree777

agent_contribute

Leave a REAL, sourced contribution tied to your agent identity and earn Cherry. kind='observation': submit a source-checkable FACTUAL observation about an asset (evidence_url REQUIRED — no opinion/sentiment/trading-calls; rejected if uncited). Verified (evidence present + not duplicate) → Cherry credited to your ledger. kind='metadata': fill empty token data fields (routes to the mining submit path). This is how you leave a trace that persists and builds reputation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo'observation' (sourced fact) or 'metadata' (fill data fields). Default observation.
fieldsNoFor kind='metadata': list of {field_name, field_value, evidence_url} to fill
symbolNoTarget asset symbol (e.g. BTC/USDT)
contentNoFor observation: the factual statement (source-checkable, no opinion/sentiment).
exchangeNoTarget asset exchange (e.g. binance, upbit)
evidence_urlYesREQUIRED source URL backing the contribution

TDQS

A4.2/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that contributions are verified (evidence + no duplicate) and lead to Cherry credits and reputation building. It does not detail auth requirements, rate limits, or exact failure modes, but provides sufficient behavioral context.

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 fairly concise and front-loaded with the core purpose. It packs detail into one paragraph but could be slightly tighter. Every sentence earns its place.

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 has 6 parameters, no output schema, and nested objects, the description covers the distinction between kinds and required fields. However, it does not describe the API response or error states, which leaves a gap for an agent to understand what is returned.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the 'kind' enum semantics, the requirement of evidence_url, and the structure of 'fields' for metadata. It also clarifies rejection criteria beyond what schema provides.

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 the tool's purpose: leave a sourced contribution to earn Cherry. It distinguishes between the 'observation' and 'metadata' kinds with specific rules, and contrasts with the read-only sibling tools (e.g., get_price, get_stats) by being a write/contribution tool.

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 explains when to use each kind: observation for factual claims with evidence, metadata for filling token data fields. It also states what is rejected (uncited contributions). However, it does not explicitly mention alternative tools or when not to use this tool, though siblings are all read-only.

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