Finance Signal Bundle - get live computed signal: Composite processed layer across 8 series: per-member 30-period percentile rank and 4-period momentum, cross-sectional ranking, plus a plain Price 0.05 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?
There are no annotations, so the description carries the burden and does add meaningful behavior: the signal is live-computed, composite across 8 series, and involves a 'plain Price 0.05 via x402 (USDC on Base)' which appears to disclose a payment/cost behavior. It stops short of explaining rate limits, authentication, or what exactly the returned price represents, but for a read-oriented signal tool this is reasonably transparent.
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 dense sentence that front-loads the core purpose and packs methodology, scope, and pricing into a compact space. The colon-heavy structure is slightly awkward and the term 'plain' adds little, but there is no wasted filler or redundant restatement.
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?
Without an output schema or annotations, the description should explain what the caller actually receives and what the input signal covers. It omits which 8 series are used, provides no return-shape information, and the 'Price 0.05 via x402' phrase is ambiguous—it could mean cost or a returned price value. This makes it incomplete for confident invocation.
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 100%, and the single optional wallet parameter is already fully documented in the input schema. The descriptions adds no parameter-level meaning beyond what the schema provides, so a baseline 3 is appropriate.
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 names a specific resource ('live computed signal') and gives a concrete computation recipe: 30-period percentile rank, 4-period momentum, cross-sectional ranking, and a price via x402. It is identifiable as a signal-bundle tool, but it never names the underlying 8 series or explicitly ties itself to FRED/economic data, so it does not fully stand apart from the many similar processed_* siblings.
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?
There is no guidance on when to use this tool versus alternatives. With dozens of processed_* and signal_* siblings, the description should say whether this is for FRED-derived economic signals, how it differs from signal_econ_global or processed_global_markets, or when the composite ranking is preferable. None of that is present.
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.