Scenario engine seed→entity→3-scenario verdict.json + composite YES prob mapped to market question. Input raw seed text + question, get bear/base/bull scenarios + fair_price_hint.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Optional context | |
| seed_text | Yes | Raw seed text / headline pack | |
| market_question | Yes | Polymarket/Yes-No market question |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With empty annotations, the description carries the burden of behavioral disclosure. It states the input-output mapping but does not discuss side effects, permissions, rate limits, or data source. No contradictions with annotations.
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, but the first sentence is dense and jargon-heavy. While not verbose, it could be clearer and more directly helpful to an AI agent.
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 covers basic input-output but lacks details on output structure, limitations, or integration with market data. Given no output schema and many siblings, more completeness would be beneficial.
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 description coverage is 100%, so the schema already describes all parameters. The description adds minimal value by repeating the input format and clarifying the output, but no new parameter semantics.
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 that the tool takes seed text and a market question, and returns bear/base/bull scenarios with a fair price hint. It distinguishes from siblings focused on market scanning or data retrieval, but uses jargon ('seed→entity→3-scenario') that may be unclear.
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 explicit guidance on when to use this tool versus alternatives. The description does not mention exclusions, prerequisites, or comparison with sibling tools like polymarket_event_scan.
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.