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madeonsol_kol_first_touches

Read-onlyIdempotent

Retrieve real-time events when a tracked KOL is the first buyer of a newly minted token. Filter by scout tier, winrate, and token age to identify early signals with high follow-on rates.

Instructions

Recent first-KOL-touch events — every time a tracked KOL was the first to buy a token mint. Filterable by scout tier (S/A/B/C from mv_kol_scout_score), KOL winrate, token age, etc. Backtest: top scouts attract ≥3 follow-on KOLs within 4h ~50% of the time vs ~14% baseline. Median lead time before second KOL is 12s — for trading this signal, use the WebSocket channel rather than polling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kolNoFilter to a single KOL wallet address (base58)
limitNoNumber of events to return (1-100, default 50)
sinceNoISO timestamp — events strictly newer than this. Polling cursor.
beforeNoISO timestamp — events strictly older than this. Pagination cursor.
presetNoShortcut filter: 'scout' = min_scout_tier=B + min_n_touches=30 + token_age_max_min=60. 'fresh_launch' = token_age_max_min=15.
includeNoComma-separated includes — currently 'followers_4h' (computed for events >=4h old)
strategyNoFilter by first-touch KOL's auto-tagged strategy
mint_suffixNoSuffix-filter the token mint (e.g. 'pump', 'bonk')
min_n_touchesNoLower the minimum sample size for scout scoring (default 30)
min_scout_tierNoRestrict to first-touch KOLs of this scout tier or better. Requires n_first_touches_30d >= 30.
min_first_buy_solNoMinimum size of the first KOL buy in SOL
token_age_max_minNoOnly events on tokens younger than N minutes (uses token_first_seen)
min_kol_winrate_7dNoMinimum 7d winrate of the first-touch KOL (0-100)
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, providing strong behavioral transparency. The description adds context like backtest stats and a WebSocket recommendation, but doesn't reveal additional behavioral traits beyond what annotations cover.

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?

Description is front-loaded with purpose and is well-structured. The backtest statistics add value but are slightly lengthy; however, every sentence contributes to understanding. Overall concise for the information provided.

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?

With 13 parameters and no output schema, the description covers purpose, filters, and usage guidance but does not describe the return fields or event structure. This leaves the agent somewhat uncertain about output format, making it less complete.

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 the description does not add new meaning beyond the input schema. It summarizes filter options but does not clarify parameter usage beyond what the schema already provides. Baseline 3 is appropriate.

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?

Description starts with 'Recent first-KOL-touch events — every time a tracked KOL was the first to buy a token mint', providing a clear verb+resource. It distinguishes from siblings like madeonsol_kol_feed and madeonsol_kol_consensus by focusing on first touches and includes filterable criteria.

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?

Description includes backtest statistics and explicitly recommends using WebSocket for trading signals, guiding the agent on when to use this tool for historical analysis versus real-time alternatives. However, it does not explicitly state when not to use the tool.

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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