Nansen

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

Name	Required	Description	Default
`request`	Yes	Request model for Nansen Score Top Tokens endpoint (flattened).

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedOct 28, 2026

addedInput schema / properties / request / additionalProperties
```
Added value: +false
```

removedInput schema / properties / request / anyOf

Removed 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"
-  }
-]

addedInput schema / properties / request / description

Added value: +"Request model for Nansen Score Top Tokens endpoint (flattened)."

addedInput schema / properties / request / properties

Added value: +{
+  "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"
+  }
+}

addedInput schema / properties / request / type
```
Added value: +"object"
```

Changed3 schema fields changedMay 19, 2026

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"
+  }
+]

First observedFeb 18, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already cover safety with readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral context not present in annotations: results are pre-filtered by Performance Score >=15, data is updated in batches, output columns are dynamic, and scoring thresholds must be explained to users.

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 front-loaded and well structured with clear sections for usage, alternatives, scoring, and returns. It is somewhat long and repeats the Performance Score >=15 threshold in multiple places, which keeps it from being maximally concise.

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?

Given the presence of annotations, an output schema, and 100% schema description coverage, the description is complete for correct invocation. It explains the ranking logic, filtering behavior, output shape, and scoring interpretation without needing to restate schema details.

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%, so the single marketCapGroup parameter is already well documented in the schema. The description does not add parameter-specific details such as market-cap filtering semantics, so the 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?

The description states a specific verb and resource: discover/filter a daily list of tokens ranked by Nansen Score. It also explicitly distinguishes this tool from token_quant_scores and token_discovery_screener, so an agent can identify its role without opening sibling schemas.

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?

It gives explicit when-to-use guidance, including the exact scenario for this tool versus token_quant_scores and a detailed comparison against token_discovery_screener. Example queries further remove ambiguity about the intended invocation context.

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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