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MadeOnSol

MadeOnSol

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madeonsol_kol_feed

Read-onlyIdempotent

Track real-time Solana KOL trades from 1,000+ wallets with on-trade market cap data. Filter by buy/sell, wallet, or PRO+ filters for size, age, strategy, and winrate.

Instructions

Get real-time Solana KOL trades from 1,000+ tracked wallets. Each trade includes the token's market cap (USD) at the moment of trade — sourced from our in-memory price tracker, accurate to the millisecond, faster than Dexscreener spot. PRO+ adds size/age/strategy/winrate filters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kolNoFilter by specific KOL wallet address (base58)
limitNoNumber of trades to return (1-100)
actionNoFilter by trade type: buy or sell
beforeNoCursor — ISO 8601 timestamp; returns trades strictly older than this. Pass next_before from the previous response for polling.
min_solNoPRO+: minimum SOL size per trade
strategyNoPRO+: filter by auto-tagged strategy
exclude_sellsNoPRO+: drop sell-side trades
min_kol_winrateNoPRO+: minimum 7d winrate of the KOL (0-100)
token_age_max_minNoPRO+: max token age in minutes at time of trade
Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context: real-time nature, data source (in-memory price tracker), speed comparison, and PRO+ filter limitations. This adds value beyond annotations without contradiction.

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?

Two well-structured sentences: first states core purpose and output details, second highlights PRO+ tiers and filters. No redundant information. Every sentence adds value and is front-loaded with essential info.

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?

No output schema exists, so description must explain return values. It mentions each trade includes token's market cap but not other fields. Given 9 parameters and complexity, the description covers the key output aspect but could list more fields. Adequate but not exhaustive.

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% with descriptions for all 9 parameters. The description groups PRO+ parameters (min_sol, strategy, exclude_sells, min_kol_winrate, token_age_max_min) under 'size/age/strategy/winrate filters', adding meaningful context about tier restrictions. This enhances understanding beyond individual parameter descriptions.

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?

The description clearly states it retrieves real-time Solana KOL trades from 1,000+ tracked wallets. The verb 'Get' and resource 'trades' are specific. However, it does not explicitly differentiate from sibling tools like kol_alerts_recent or kol_leaderboard, which might have overlapping purposes.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. The description mentions PRO+ features but does not specify exclusions or contexts where other tools might be preferred. The user is left to infer use cases from the tool name and context signals.

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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