get_marketGet one PredictIt market by id with ALL of its contracts. Contract prices are implied probabilities (0–100%). Keyless. Example id: 7589 (2026 Republican House seats). Use list_markets or search_markets to find ids.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | A PredictIt market id, e.g. 7589 or 8155 (Senate control 2026). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that contract prices are implied probabilities (0–100%) and that the tool is keyless, which enriches behavioral understanding beyond 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, front-loaded with the primary action. No extraneous information. Every sentence adds value.
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 one parameter and no output schema, the description covers all needed: what it does, how to use it, a data detail, and how to find inputs. Annotations further support safety and idempotency.
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 covers type and description with 100% coverage. The description adds concrete examples (e.g., 7589 for 2026 Republican House seats) and clarifies the parameter's meaning, adding value beyond the schema.
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?
Clearly states verb ('Get'), resource ('one PredictIt market by id'), and scope ('with ALL of its contracts'). Distinguishes from siblings by referencing list_markets and search_markets for finding ids.
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?
Explicitly tells when to use this tool (to get a market by id) and when not (to find ids, use list_markets or search_markets). Provides an example id for context.
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.