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pr1m8

polymarket-mcp

by pr1m8

gamma_search_public

Search Polymarket markets by describing a topic in plain text. Returns normalized markets to refine further with exact slug lookups.

Instructions

Search Polymarket discovery data by free-text topic.

Use this tool when you know a topic or phrase but do not yet know the exact market slug. This is the best first step for queries like "Fed decision", "NBA finals", or "election odds".

Prefer this tool over get_market_by_slug when you only have a natural language description. Prefer list_markets when you already have a more structured filter such as a known tag or exact slug.

The query should be plain human text, not a full URL. The result returns normalized markets that can be followed up with get_market_by_slug for a more precise single-market lookup. If you need live pricing or the order book next, use the CLOB public server after you obtain the market's token IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsYesStructured free-text search arguments.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
marketsNo
countYesReturn the number of matching markets. Returns: int: Number of returned markets. Raises: None. Examples: >>> SearchMarketsOutput(query="x", markets=[]).count 0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations; description implies read-only search behavior and mentions normalized market results, but does not explicitly disclose auth requirements, rate limits, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured, front-loaded with purpose, then usage guidance and preferences, without unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers usage context, alternatives, query format, follow-up actions, and relies on output schema for return details, making it complete for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%; description adds that query should be plain text (not URL), enhancing the schema's minimal info. Limit is not elaborated, but overall adds value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches Polymarket discovery data by free-text topic, and distinguishes from siblings by specifying when to use this tool versus get_market_by_slug and list_markets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs to use when a topic is known but not the exact slug, gives example queries, and advises preferring this over alternatives with clear conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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