Fetch a data product. Pass wallet (0x...) to use the free trial / paid access; without payment an x402 402 payment challenge is returned.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| wallet | No | optional 0x wallet for X-Wallet free trial |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the full burden. It clearly discloses the key behavioral trait: without payment, an x402 402 payment challenge is returned, and wallet enables the free trial/paid path. It does not mention error/return formats, but the payment behavior is a material and well-communicated detail.
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?
A single, front-loaded sentence conveys purpose, the optional wallet parameter, and the payment-challenge behavior without waste. Every clause earns its place.
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?
For a simple two-parameter fetch tool with no output schema/annotations, the description is adequate but not fully complete. It explains the payment gate but does not describe the successful response shape or how to discover the id (e.g., via list_products), leaving some ambiguity for the agent.
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 50%: wallet is described, id is not. The description adds payment and trial meaning for wallet and implies id identifies the data product, but id's format and requirement remain under-specified. It partially compensates for the low schema coverage.
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 states a specific action ('Fetch a data product') with a clear resource and the relevant id/wallet context. It does not explicitly differentiate from siblings like get_product_info or list_products, but 'fetch' and the payment challenge make the data-delivery purpose clear.
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 gives usage context around wallet and payment ('Pass wallet... to use the free trial / paid access; without payment... challenge is returned'), but it does not state when to prefer fetch_data over sibling tools or list exclusions. Usage is implied rather than explicitly contrasted.
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.
Nearly all 80+ processed_/signal_ tools share the same boilerplate description and overlapping coverage areas (e.g., processed_global_markets vs. processed_global_indices vs. signal_global_indices; processed_crypto_funding_rate vs. processed_crypto_funding_rates), so an agent cannot reliably distinguish them. The generic ai_gateway, fetch_data, and list_products also have somewhat fuzzy boundaries around data access. Only broad asset categories in the names provide any separation.
Core tools use verb-first imperative names (fetch_data, get_product_info, list_products), while the vast majority use adjective/noun prefixes (processed_*, signal_*), so the set mixes conventions. Within each cluster naming is consistent and all names are readable snake_case, but the 87-tool surface has no single predictable verb_noun pattern. Minor singular/plural inconsistencies like processed_crypto_funding_rate vs. processed_crypto_funding_rates add friction.
87 tools is an extremely large surface for an MCP server, and most are variant data products that could be one fetch_data call with a product identifier. This falls into the >50 extreme range. The generic list/get/fetch primitives make the 80+ product-specific endpoints especially redundant.
The core consumer flow is covered: list products, inspect product info, and fetch a product (with payment challenge handling). Missing wallet/credit/balance tools and search/filtering are notable but work-aroundable. For a read-only data marketplace the lifecycle is largely complete.