[marketsummary] Where is the market conversation concentrated? Narrative-energy grid over our 6 recap categories (market / whale / news / tech / regulation / funding). Free = hottest + coldest; premium = the full grid + crypto/equity split. Energy = item count, NOT sector returns. 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 are provided, so the description carries the burden. It discloses that 'energy = item count, NOT sector returns' and mentions free vs premium tiers, which adds value. However, it does not discuss latency, data source, caching, or whether the tool is read-only.
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 three sentences, front-loaded with an engaging question. It efficiently conveys the core idea, tiers, and a key clarification. Minor verbosity ('Not financial advice') is acceptable.
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 2 undocumented parameters, no output schema, and many siblings, the description lacks completeness. It does not explain the output format, parameter usage, or when to prefer this tool over alternatives. The free vs premium distinction is helpful but insufficient.
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, the description must explain the parameters. It does not mention 'payment' (likely for tier) or 'session_token' (likely for authentication) at all. No added meaning beyond the schema's default values.
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's purpose: showing where market conversation is concentrated via a narrative-energy grid across six categories. It distinguishes from siblings by focusing on a heatmap of energy (item count) over fixed recap categories, not sector returns or buzz scores.
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 usage when you want narrative concentration across categories, but it does not provide explicit when-to-use, when-not-to-use, or alternatives. Siblings like 'get_buzz_score' or 'track_narrative' could overlap, but no guidance is given.
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.