Finance Signal Bundle - get live computed signal: Momentum/quantile signals for geomagnetic Kp index (0-9) with 24 points history, warning of solar activity impact on communications, power g 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?
With no annotations provided, the description bears full responsibility for disclosing behavior. It reveals that the signal is 'live' (computed in real-time), includes '24 points history', and costs '0.01 via x402 (USDC on Base)', which are meaningful behavioral traits. However, it does not describe the result format, error behavior, or any rate limits, leaving significant gaps in transparency.
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 disjointed clauses and a clear truncation ('power g Price'), making it poorly structured. While it packs several pieces of information, the lack of segmentation and the broken ending undermine readability and conciseness.
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?
The tool has no output schema, so the description should clarify return values, but it only loosely mentions 'signal' and 'history'. It also fails to explain the payment flow implied by 'Price 0.01 via x402' and does not clarify the truncated 'power g' warning. The description is incomplete and would leave an agent uncertain about the tool's full behavior and output.
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 already documents the single optional 'wallet' parameter with its own description, achieving 100% coverage. The tool description does not mention the wallet parameter or add any further semantics, but with high schema coverage the baseline score of 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 clearly identifies the tool as providing a live computed financial signal based on geomagnetic Kp index, with a specific verb ('get') and resource ('live computed signal'). It distinguishes itself from sibling signals by focusing on solar activity's impact on markets/infrastructure. However, the truncated phrase 'power g' and the inclusion of pricing details slightly muddle the core purpose, preventing a perfect score.
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 monitoring solar activity impacts on communications and power infrastructure, which provides a context for when to call this tool. However, it does not explicitly state conditions for use or mention alternative sibling tools, so the guidance is only implicit rather than explicit.
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.