Finance Signal Bundle - get live computed signal: Momentum/quantile/volatility signals for spot electricity prices in Finland, Sweden, and Denmark (48-96 hourly points), reflecting European Price 0.01 via x402 (USDC on Base).
| Name | Required | Description | Default |
|---|---|---|---|
| wallet | No | optional 0x wallet for X-Wallet free tier (free credits every month: 100 anonymous or 5000 with a bound wallet) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It mentions 'live computed signal' and data granularity (48-96 hourly points), but the clause 'reflecting European Price 0.01 via x402' is cryptic and does not clearly explain if the tool incurs costs, whether it is read-only, or what the output structure looks like. This leaves significant ambiguity.
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 single run-on sentence with awkward phrasing (e.g., 'reflecting European Price 0.01' with a double space). It tries to convey too many elements in one breath, making it less scannable than a structured two-sentence format would be.
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?
With no output schema, the description should explain what the returned signal looks like and clarify the pricing mechanism. It does neither clearly. It also omits any mention of the optional wallet parameter, which is part of the free tier context. The description is adequate for basic purpose but incomplete for a tool with a possible payment component.
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 describes the only parameter (wallet) with detailed information about free tier credits, achieving 100% schema description coverage. The description adds no parameter-specific meaning, but the baseline of 3 is appropriate given the schema's thoroughness.
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 function: it provides live computed signals (momentum/quantile/volatility) for spot electricity prices in Finland, Sweden, and Denmark. The specific geographic scope and signal types distinguish it from sibling tools like signal_energy or signal_macro_us.
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 for electricity price signals but lacks explicit guidance on when to use this tool versus alternatives. It does not name any sibling tools or provide exclusion criteria, leaving the agent to infer suitability from the geographic and commodity-specific context.
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.