Attention momentum — HN Algolia + Reddit JSON velocity scoring, points/hr + momentum_score, viral flag for culture/pop/app-rank/YouTube/npm-dl Polymarkets. Proxies: pypistats, api.npmjs.org.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Topic filter e.g. Claude Code | |
| window | No | 1h|6h|24h, default 6h |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so the description carries full burden. It indicates the tool performs scoring and computes viral flags but does not disclose side effects (e.g., readOnly status), rate limits, or data sources beyond mentioning proxies. Limited behavioral disclosure.
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 lines covering key outputs and sources, front-loaded. Some jargon ('proxies', 'Polymarkets') reduces clarity, but overall 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?
No output schema exists, so the description should explain return values. It mentions velocity scoring, momentum_score, and viral flag but lacks format details and clarity on 'proxies'. Adequate but not fully complete for an agent.
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%, providing parameter descriptions. The description adds minimal value beyond the schema, only contextualizing 'query' as a topic filter and 'window' as time range. Baseline score applies.
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 states the tool computes attention momentum scores from HN and Reddit, with outputs like points/hr, momentum_score, and viral flag. While specific, the jargon ('Polymarkets', 'proxies') may obscure the purpose, and it does not differentiate from sibling tools like reddit_search or hn_frontpage.
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?
No guidance on when to use this tool versus alternatives. The description lacks explicit when-to-use/when-not-to-use instructions or comparisons to siblings.
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.
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.