MAD Synapse

DLMM LP whales

dlmm_whales

Read-onlyIdempotent

Meteora DLMM liquidity providers ranked by measured on-chain edge: net USD, median multiple, win rate, hold time, size, recency. Produced by our own discovery engine, which decodes DLMM liquidity instructions across high-volume and high-yield pools every few hours and scores each wallet's completed round trips. These are the wallets our live copier watches. Price: $0.02 per call (10 free/day; after that a payment-required result lists x402 options). Errors: returns isError with a message for invalid input or an upstream failure (not charged).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	How many ranked LP wallets to return. Range 1-25. Default 10.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`method`	No
`whales`	No
`generated_at`	No
`pools_scanned`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 27, 2026

addedInput schema / properties / limit / description

Added value: +"How many ranked LP wallets to return. Range 1-25. Default 10."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "generated_at": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "method": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "pools_scanned": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "whales": {
+      "items": {
+        "properties": {
+          "avg_trade_usd": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "last_seen": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "median_hold_h": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "median_multiple": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "net_usd": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "pools_traded": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "roundtrips": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "score": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "wallet": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "win_rate_pct": {
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "type": [
+          "object",
+          "null"
+        ]
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}

First observedSep 25, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Annotations already cover the read-only/idempotent safety profile, and the description adds substantial context beyond them: data provenance (own discovery engine decoding DLMM instructions across high-volume/high-yield pools), refresh cadence (every few hours), cost model ($0.02/call, 10 free/day, x402 payment-required result), and error behavior (isError, not charged). This is exactly the extra behavioral context the bar rewards.

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?

Front-loads the identity and ranking criteria, then provenance, then pricing, then error handling. Dense but every sentence carries distinct information; slightly packed for a single paragraph but no wasted filler.

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?

An output schema exists, so return-value explanation is unnecessary, and the description fills the remaining gaps an agent needs: cost/quota, failure semantics, data freshness, and the source of the ranking. Nothing required for correct invocation is missing.

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% for the single 'limit' parameter, including default and 1-25 range, so the schema already carries full semantics. The description adds nothing about the parameter, which is the correct baseline when the schema does the heavy lifting.

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?

States a specific verb+resource (Meteora DLMM liquidity providers) and spells out the ranking dimensions (net USD, median multiple, win rate, hold time, size, recency). An agent can distinguish it from dlmm_pool, dlmm_positions, and smart_money purely from the description.

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

Usage Guidelines3/5

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

Provides useful context ('these are the wallets our live copier watches') that implies a copy-trading/discovery use case, and pricing guidance is explicit. However, it never states when to pick this over siblings like smart_money or dlmm_positions, nor any exclusions. Usage is implied rather than directed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All MAD Synapse tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?