Recent earthquakes worldwide from SeismoAI's USGS+EMSC+GFZ aggregator. Returns magnitude, location, depth, time, source. Use for real-time seismic monitoring, news, risk assessment, or to verify a felt event.
| Name | Required | Description | Default |
|---|---|---|---|
| hours | No | Lookback hours (1-168) | |
| limit | No | Max results (1-100) | |
| minMagnitude | No | Min magnitude (0-10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description does not disclose behavioral traits such as rate limits, authorization, or side effects. It only describes output fields and use cases, insufficient for full transparency burden.
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?
Two sentences with zero waste. First sentence states purpose and output, second gives use cases. Front-loaded and efficient.
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 tool with 3 optional params and no output schema, the description covers purpose, data sources, use cases, and output fields. Missing details like timezone or pagination are minor given the context.
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 coverage is 100%, so the schema fully documents parameters. The description adds no parameter-specific meaning beyond listing output fields, which is not required. 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 clearly states the tool returns recent earthquakes worldwide from a specific aggregator, listing returned fields and use cases. It distinguishes from sibling 'alya_seismic_forecast' by focusing on real-time events.
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?
Explicit use cases are given (real-time monitoring, news, risk assessment, verification), but no direct guidance on when not to use or alternatives beyond implied sibling differentiation.
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.
Most tools have distinct purposes and clear descriptions, but there is some potential confusion among the four Polymarket-related tools (categorize, edge, signals, top_traders) and among the multiple 'alya_' prefixed tools that query different data sources.
Naming patterns are mixed: some tools use 'alya_' prefix, others use action-based names like 'batch_calibrate' or 'image_gen', and YouTube tools all start with 'youtube_'. The inconsistency in prefixes and verb styles makes the set less predictable.
32 tools is high for an MCP server, and they span a wide, unrelated set of domains (Polymarket, YouTube, gemology, weather, earthquakes, health, celebrity, etc.), making the surface feel bloated and unfocused.
Each domain has incomplete coverage: Polymarket lacks trade execution, YouTube automation depends on external OAuth, health tools only offer diagnosis and drug interactions without follow-up, and other domains have minimal tooling. The server feels like a collection of one-off features rather than a coherent surface.