[marketsummary] Ask any market question in natural language and get a grounded answer.
lang: en / pl. Free returns a connect-CTA. Unlock a grounded answer via session_token (from
verify_wallet_ownership) OR an x402 payment proof.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | en | |
| payment | No | ||
| question | Yes | ||
| session_token | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that free usage returns a connect-CTA and that a grounded answer is unlocked via session_token or payment proof. This meaningfully informs the agent of auth-dependent behavior, though it does not mention idempotency or side effects.
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 a concise paragraph (two sentences) that efficiently conveys purpose, usage modes, and language options. It is front-loaded with the core purpose and adds details without excess. Slight improvement could be made by separating parameters more clearly.
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 4 parameters, the description covers the essential aspects: what it does, how to get results, and language support. It lacks explicit return format or error behavior, but these are secondary for a straightforward question-answering tool. The description is adequate for an agent to select and invoke it 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?
Schema description coverage is 0%, so the description must compensate. It explains 'lang' (en/pl) and the auth parameters (session_token from verify_wallet_ownership, x402 payment proof). However, it does not describe the 'payment' default (empty string) or the 'question' parameter beyond implying it holds the natural language query.
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: 'Ask any market question in natural language and get a grounded answer.' It uses a specific verb ('ask') and resource ('market'), distinguishing it from sibling tools that handle specific queries like get_digest or get_trending.
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 (for general market questions) and explains how to access different levels of results: free returns a connect-CTA, while a grounded answer requires session_token or payment. However, it does not explicitly list when-not-to-use or compare to siblings.
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.