Finance Signal Bundle - get live computed signal: Momentum, volatility, z-score, and percentile signals for 10 US mega-caps like Apple, Microsoft, Nvidia, Tesla, with 30-day trends. 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?
With no annotations, the description carries the burden of behavioral disclosure. It transparently reveals the paid nature (0.05 via x402 USDC on Base) and the optional free tier for wallet-bound users, which is critical for an agent to anticipate costs and prerequisites. However, it does not describe the exact response structure or rate limits.
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 two sentences, front-loaded with the core purpose, and every phrase adds value: signal types, examples, trends, price, and payment method. There is no filler or repetition beyond a slight redundancy in 'Finance Signal Bundle'.
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?
While the description mentions the signals and trends, there is no output schema, and the expected response format is not described. For a paid tool, an agent might need to know whether the output is an array, a JSON object, or includes timestamps. The description gives enough to infer purpose but not enough to fully invoke and parse the response.
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 only parameter (wallet) is fully described in the schema with its free-tier details. The tool description adds the cost context but does not add new meaning beyond the schema. Baseline 3 is appropriate since schema coverage is 100%.
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 a live signal bundle for US mega-cap stocks, listing specific signal types (Momentum, volatility, z-score, percentile) and example tickers (Apple, Microsoft, Nvidia, Tesla). This is specific enough to distinguish it from sibling tools like signal_global_indices or signal_gold.
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 use for US mega-cap equity signals but does not explicitly state when to choose this over alternatives such as signal_stock_report or processed_* tools. There is no mention of exclusions or specific use cases, so guidance is 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.