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mcp-knowledge

by io.github.getgapup

polymarket_markets

Query live Polymarket prediction markets, ranked by volume. Returns question, implied probability (0-1, derived from the outcome price), volume, liquidity, end date and URL. Optional free-text filter on the question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
limitNoMaximum markets (default 20)
queryNoFree-text filter on the market question
includeRawNoInclude Polymarket's original fields (default false)
includeClosedNoInclude settled markets (default false)

TDQS

A4/5.0
Behavior3/5

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

The description lists the return fields (question, implied probability, volume, liquidity, end date, URL) and notes the probability is derived from outcome price. With no annotations provided, this offers some transparency, but it does not disclose potential latency, rate limits, or the behavior of the 'async' parameter (which is present in the schema but not explained).

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?

The description is two sentences with no wasted words. It front-loads the core purpose and compacts the return fields and filter capability into a clear, scannable structure.

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

Completeness4/5

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

For a simple query tool with five optional parameters and no output schema, the description gives a solid overview of the tool's purpose and return data. It lacks guidance on sorting (beyond 'ranked by volume'), pagination behavior, or async usage, which are minor gaps given the tool's simplicity.

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

Parameters3/5

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

The schema description coverage is 100%, so the parameters are already well-documented. The description adds minimal value by restating the optional free-text filter on the question, but it does not explain parameter interactions or provide additional context beyond the schema.

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 uses a specific verb ('Query') and resource ('live Polymarket prediction markets'), with additional detail about ranking by volume. This clearly distinguishes it from sibling tools like 'polymarket_events' (events) and 'kalshi_markets' (a different prediction market), making the purpose unambiguous.

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

Usage Guidelines4/5

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

The description clearly indicates this is for querying live Polymarket markets ranked by volume, which establishes a clear use case. However, it does not explicitly mention alternatives or when not to use this tool, though the focus on 'live' markets implies exclusions for historical or non-volume-ranked queries.

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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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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