search_marketsSearch PredictIt markets by name (client-side over all ~250 markets; PredictIt has no search endpoint). US-politics-focused. Contract prices are implied probabilities (0–100%). Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max markets to return (default 15, max 30). | |
| query | Yes | Case-insensitive substring, e.g. "Senate", "Trump", "2026". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds: client-side execution (no external API), scope of all markets, interpretation of prices as probabilities, and keyless access. 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?
The description is two sentences, each adding essential information without redundancy. It front-loads the main purpose and adds contextual details efficiently.
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?
Given no output schema, the description explains that results include contract prices as implied probabilities. It covers scope, limitations (no PredictIt endpoint), and authentication. Slightly more detail on output format could push to 5, but current is sufficient.
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 detailed descriptions for both parameters. The description adds no new information beyond the schema (case-insensitive substring, defaults). Baseline 3 is appropriate as the schema bears the explanatory burden.
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 'Search PredictIt markets by name', providing a specific verb and resource. It distinguishes from siblings like 'get_market' and 'list_markets' by noting it performs client-side search over all ~250 PredictIt markets and that PredictIt lacks a search endpoint.
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 gives clear context: it's client-side, covers ~250 markets, and is US-politics-focused. It also mentions 'Keyless' for auth. However, it does not explicitly state when to prefer this over sibling tools like get_market or list_markets, though the scope is implied.
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, but pairs like ask_pipeworx/ask_pipeworx_grounded and bet_research/polymarket_edges could cause confusion without careful reading. Overall, descriptions are clear enough to differentiate.
Names are snake_case and mostly follow verb_noun pattern, but several are noun_noun (entity_profile, pipeworx_feedback, polymarket_arbitrage) creating inconsistency. Still readable due to descriptive terms.
33 tools is slightly high but justified given the broad scope (data retrieval, prediction markets, memory, subscriptions). Each tool serves a specific role, so the count feels appropriate for the platform's capabilities.
The tool set covers data retrieval, prediction market analysis, memory management, and subscriptions well. Minor gaps exist (e.g., no direct betting tool), but core workflows are supported comprehensively.