[marketsummary] Trending tokens — the most-mentioned tickers.
timeframe: 24h / 7d / 30d. Free returns the top 3 tickers; the full ranking (top 15 with counts)
unlocks via session_token (from verify_wallet_ownership) OR an x402 payment proof.
| Name | Required | Description | Default |
|---|---|---|---|
| payment | No | ||
| timeframe | No | 7d | |
| session_token | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses important behavioral traits: free tier returns top 3, full ranking requires session_token or payment proof. This goes beyond the schema by explaining access restrictions, though it does not mention rate limits or data freshness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences that front-load the purpose and cover key details. Every sentence adds value without repetition or unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and 0% schema coverage, the description provides sufficient context: what the tool returns (top tickers with counts for full ranking), timeframe options, and access tiers. It could mention return structure (e.g., list of tickers with counts) but is adequate for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It explains the timeframe parameter options (24h, 7d, 30d) and the role of session_token and payment for unlocking. It adds value but does not fully specify formats or exact allowed values beyond the examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'Trending tokens — the most-mentioned tickers,' using a specific verb and resource. It distinguishes itself from sibling tools like marketsummary__get_buzz_score or marketsummary__get_attention_shifts by focusing on overall trending tokens based on mention count.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (to get trending tokens by mentions) and explains the difference between free (top 3) and full ranking (top 15) via session_token or payment. However, it does not explicitly compare with alternative tools or specify when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of market analysis or token safety, with clear descriptions that avoid overlap. Tools like find_longs and find_shorts are opposites, and other get_* tools each address unique metrics (e.g., buzz, attention shifts, smart money flow). No two tools have ambiguous boundaries.
Tools are prefixed by category (marketsummary__, tokensafety__, verify__), and most use a verb_noun pattern (e.g., get_liquidity, find_longs). However, there is some variation: 'ask_market', 'track_narrative', and longer names like 'attention_vs_price_divergence' break the pure 'get_' pattern. Overall, the naming is logical and predictable.
With 24 tools, the server covers a wide range of crypto market analysis, from general summaries to specific signals (e.g., conviction gap, smart money flow) and token safety. The count is well-scoped for the domain; each tool serves a clear purpose without being excessive.
The tool set is comprehensive for a market analysis server: it includes natural language querying, trend detection, sentiment, liquidity, whale activity, safety checks, and wallet verification for premium access. Missing trading actions are outside the stated purpose, so no gaps are apparent.