Listing token sectors
token_sectorsList valid token sectors for token discovery filters.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Nansen
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- Added
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It communicates a read-only, side-effect-free enumeration action with 'List' and adds context by specifying 'valid' and 'for token discovery filters.'
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?
The description is a single, front-loaded sentence with no wasted words. It conveys the action, scope, and purpose efficiently.
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?
For a zero-parameter reference tool with an output schema, the description is complete. It states what the tool returns and why it would be used, which is sufficient context for an agent.
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?
The tool has zero parameters, so there is no parameter semantics burden on the description. With no input schema properties to document, the baseline of 4 applies.
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 uses a specific verb and resource: 'List valid token sectors for token discovery filters.' It clearly identifies the tool's purpose, though it does not explicitly differentiate from sibling tools like token_discovery_screener.
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?
The phrase 'for token discovery filters' gives clear usage context, indicating this tool supplies valid values for discovery filtering. It does not explicitly mention alternatives or exclusions, but the intended use is apparent.
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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TDQS
A3.8/5.0
Disambiguation5/5
Each tool has a clearly defined purpose, and overlapping tools (e.g., token_flows vs token_recent_flows_summary, nansen_score_top_tokens vs token_discovery_screener) include explicit guidance on when to use them. Even with similar names like prediction_market_trades and prediction_market_address_trades, the descriptions and parameters make the distinction unambiguous.
Naming Consistency4/5
Most tools follow a domain_prefix_noun pattern (address_, token_, prediction_market_), making them predictable within families. However, outliers like general_search, growth_chain_rank, hyperliquid_leaderboard, and transaction_lookup break the pattern, and some names are long or inconsistently formatted (e.g., smart_traders_and_funds_perp_trades vs smart_traders_and_funds_token_balances).
Tool Count3/5
With 38 tools, the server is far above the typical 3-15 range, making it heavy for agents to navigate. However, Nansen is a broad analytics platform covering wallets, tokens, prediction markets, and smart money activity, so the high count is justifiable as each tool serves a distinct function.
Completeness5/5
The tool set provides comprehensive coverage across token analysis (ohlcv, trading, holders, flows, PnL, technicals), wallet analysis (portfolio, transactions, counterparties), prediction markets (lookup, orderbook, trades, PnL), and discovery. The only obvious omission is NFT support, but it is explicitly documented as out of scope, so no critical dead ends exist.