[marketsummary] What is heating up or cooling down — source-INDEPENDENT attention (mention velocity, 24h vs 7d baseline). Free = top 3 rising tickers; premium = the full rising/cooling ranking with velocity. Our attention signal does not depend on any single platform's API. Sparse data → coarse. Not advice.
| Name | Required | Description | Default |
|---|---|---|---|
| payment | No | ||
| session_token | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides valuable behavioral context: source-independence, reliance on mention velocity, 24h vs 7d baseline, free vs. premium limitations, and data sparsity leading to coarse output. It also includes a disclaimer ('Not advice'). This exceeds minimal transparency.
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 reasonably concise, front-loading the core purpose and adding key details in subsequent sentences. However, the phrasing is slightly informal and could be more structured (e.g., bullet points).
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?
The description covers what the tool returns (rising/cooling rankings with velocity) and mentions limitations, but lacks specifics on output format for premium tier and does not explain how to interpret the velocity metric. Given no output schema, more completeness would be helpful.
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?
The schema has 0% coverage for parameters; the description partially compensates by hinting that 'payment' relates to free/premium access but does not explain 'session_token'. Without mapping parameters to their purpose, the agent may misuse them.
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 retrieves attention shifts (heating/cooling) based on mention velocity, differentiating it from sibling tools like get_trending or get_buzz_score. However, it uses a question format rather than a direct verb phrase, slightly reducing clarity.
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 implies the tool is for attention shift analysis and mentions free vs. premium tiers, but does not explicitly state when to use it over alternatives or provide exclusion criteria. Usage guidance is implied rather than explicit.
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.