[marketsummary] Where our editorial desk's coverage leads or lags the crowd's chatter. desk_ahead = we cover it more than the crowd talks about it; crowd_ahead = the reverse. A cadence comparison nobody else can compute (our digest_selections vs mentions), NOT a buy signal. Free = 2 desk-ahead names; premium = both lists with the gap. Not financial advice.
| Name | Required | Description | Default |
|---|---|---|---|
| payment | No | ||
| session_token | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full responsibility. It discloses the tool's non-financial-advice nature, the meaning of 'desk_ahead' vs. 'crowd_ahead', and the output differentiation by payment tier. It does not cover error handling or authentication needs, but the core behavior is well explained.
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 concise but not overly terse; every sentence adds value (purpose, distinction, access levels, disclaimer). It could be slightly more streamlined, but it is well-organized and front-loads key information.
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 the absence of an output schema and rich annotations, the description adequately explains the output concepts and access tiers. However, the complete omission of parameter semantics leaves a significant gap for an agent attempting to invoke the tool correctly.
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?
With 0% schema description coverage and no mention of the two parameters ('payment', 'session_token') in the description, an agent receives no guidance on what values to provide or their significance. The description adds no meaning beyond the schema's bare structure.
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 defines the tool's purpose: comparing editorial coverage ('desk_ahead') to crowd chatter ('crowd_ahead') using a unique proprietary computation. It uses specific verbs ('leads or lags') and explicitly distinguishes the tool from siblings by highlighting its exclusive capability ('nobody else can compute').
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 states it is 'NOT a buy signal' and explains free vs. premium access, providing context for when to use the tool. However, it does not explicitly state when not to use it or offer alternatives among siblings, so it lacks full exclusion guidance.
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.