get_volume_spikesPolymarket markets with abnormal 24h volume vs their 7-day daily average. Volume spikes typically precede news events or informed positioning.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results to return (default: 15) | |
| min_ratio | No | Minimum ratio of 24h volume vs 7-day daily average to qualify as a spike (default: 3x) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and openWorldHint=true, indicating a safe read with changing results. The description adds that volume spikes typically precede news events, offering behavioral context beyond the annotations. No contradiction.
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 concise (two sentences) and front-loaded with the core function. Every sentence adds value: the first defines the tool, the second explains its significance.
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 description lacks details about the return format (e.g., market IDs, volume figures). Given no output schema, this information would aid completeness. However, the context signals show 100% parameter coverage and simplicity, so a score of 3 is fair.
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%, with both parameters (limit, min_ratio) having clear descriptions. The description mentions 'abnormal 24h volume vs their 7-day daily average', which relates to min_ratio, but adds little extra meaning beyond the schema. 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 Polymarket markets with abnormal 24h volume compared to their 7-day daily average. It uses a specific verb ('get') and resource ('markets with volume spikes'), and adds context about what volume spikes signify, distinguishing it from sibling tools like 'get_movers' or 'get_market_context'.
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 provides context that volume spikes typically precede news events or informed positioning, helping infer when to use the tool. However, it does not explicitly state when not to use it or mention alternatives, which would improve clarity.
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.
Many tools are specialized, but several pairs have fuzzy boundaries: e.g., get_funding_rates vs get_top_funding_rates, get_basic_macro vs get_macro_context, get_simple_iv vs get_options_iv. An agent could easily select the wrong one.
Most tools follow a 'get_X' pattern with descriptive noun phrases. There are a few exceptions like 'create_api_key' and 'search_markets', but overall the convention is consistent and readable.
With 47 tools, the server is overloaded. While the domain is broad, this many tools makes discovery and selection difficult for an agent, reducing coherence.
The tool set covers an impressively wide range: macro data, funding, prediction markets, OI history, whale tracking, risk analytics, position sizing, backtesting, and signal generation. It leaves no obvious gaps for a crypto trading assistant.