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Ranking top tokens by Nansen Score

nansen_score_top_tokens
Read-only

Discover and filter a daily list of attractive tokens using Nansen Score Indicators weighted by coefficients (= Performance Score).

Use this tool when you don't know which tokens to buy and need recommendations based on backtested indicators. For specific token analysis (e.g., "should I buy AAVE?"), use token_quant_scores instead.

When to use this tool vs token_discovery_screener:

  • Use this tool when you want pre-scored buying recommendations without specifying criteria. It answers "what should I buy?" by returning tokens that already meet a quantitative buying threshold (Performance Score ≥15) based on alpha indicators like price momentum, chain fees, and protocol fees. Data is updated in batches.

  • Use token_discovery_screener when you want live data or to explore tokens by specific criteria like sectors (e.g., "AI memecoins"), token age (e.g., "new launches"), smart money activity, or custom volume/liquidity thresholds. It's a filtering tool with real-time metrics where you define what you're looking for.

Returns tokens pre-filtered by: performance_score >= 15 (buying threshold).

Example queries: "what tokens should I buy?", "which tokens look good?", "best tokens to buy today"

Scoring:

  • Performance Score (range -60 to +75): Higher = better alpha opportunity. Buy threshold: ≥15

  • Risk Score (range -60 to +80): Higher = safer token. >0 indicates low to medium risk.

Every time you give the Performance Score to the user, explain the scoring thresholds above. Same for the Risk Score. Every time quote the underlying indicators that contributed the most to the Performance/ Risk score and recall their definition to the user.

Returns: A list of tokens with the highest Performance Score as markdown.

Core fields: Token Address, Token Symbol, Chain, Performance Score, Risk Score.
Indicator columns are included dynamically based on data availability (columns with all zeros are excluded).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • removedInput schema / $defs
      Removed value: -{
-  "NansenScoreTopTokensRequest": {
-    "description": "Request model for Nansen Score Top Tokens endpoint (flattened).",
-    "properties": {
-      "marketCapGroup": {
-        "anyOf": [
-          {
-            "enum": [
-              "lowcap",
-              "midcap",
-              "largecap"
-            ],
-            "type": "string"
-          },
-          {
-            "type": "null"
-          }
-        ],
-        "default": null,
-        "description": "Filter by market cap group: 'lowcap' (<$100M), 'midcap' ($100M-$1B), 'largecap' (>$1B). Default: all groups"
-      }
-    },
-    "type": "object"
-  }
-}
    • addedInput schema / additionalProperties
      Added value: +false
    • changedInput schema / properties / request / anyOf
      Previous value: -[
-  {
-    "additionalProperties": true,
-    "type": "object"
-  },
-  {
-    "$ref": "#/$defs/NansenScoreTopTokensRequest"
-  }
-]New value: +[
+  {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  {
+    "description": "Request model for Nansen Score Top Tokens endpoint (flattened).",
+    "properties": {
+      "marketCapGroup": {
+        "anyOf": [
+          {
+            "enum": [
+              "lowcap",
+              "midcap",
+              "largecap"
+            ],
+            "type": "string"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Filter by market cap group: 'lowcap' (<$100M), 'midcap' ($100M-$1B), 'largecap' (>$1B). Default: all groups"
+      }
+    },
+    "type": "object"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and non-destructive behavior. The description adds valuable behavioral context beyond that: daily batch updates, the performance_score >= 15 pre-filter, score ranges, dynamic indicator columns, and markdown output. No contradictions with annotations.

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 long but well organized with bold section headers, bullets, and example queries. Some redundancy exists (the performance_score >= 15 threshold appears twice, and the output format is stated more than once), but the structure keeps it skimmable.

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?

The description covers scoring semantics, return fields, dynamic columns, update cadence, and sibling-tool alternatives. It misses concrete guidance on how to pass the request payload and any pagination/result limits, but the output schema and annotations fill some gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description should compensate, but it never explains the required 'request' object or the optional marketCapGroup parameter. The only hint is the generic word 'filter', which does not tell an agent how to structure input.

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 opens with a specific verb and resource: 'Discover and filter a daily list of attractive tokens using Nansen Score Indicators.' It clearly differentiates itself from token_quant_scores and token_discovery_screener, and states the core output: pre-filtered buy recommendations with Performance Score >= 15.

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

Usage Guidelines5/5

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

The description has an explicit 'When to use this tool vs token_discovery_screener' section with concrete conditions: use this for pre-scored recommendations without criteria, use the screener for live data or custom filters. It also tells agents to use token_quant_scores for specific token analysis and provides example queries.

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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