skipseek

List covered venues

list_platforms

Read-only

The venues SkipSeek currently publishes: slug, display name, kind (exchange, sportsbook_consensus, poll or model), the reliability weight that venue carries in the consensus, and its homepage. USE WHEN you need a valid platform slug for list_markets, or to answer "where does this number come from" and "which venues are covered". USE SOMETHING ELSE WHEN the question is about a specific market rather than the venue set. Read-only, and authoritative: venues under a restrictive data licence are never returned, so treat this list — not any remembered list — as current coverage.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description
`tier`	No
`notice`	No	Set INSTEAD of data when the call succeeded but has nothing to return: unknown slug, empty result, a tool not on this tier, or quota spent. Not an error.
`source`	No
`platforms`	No	Current coverage, authoritative: venues under a restrictive licence are never returned.
`attribution`	No
`generated_at`	No
`requests_remaining_today`	No

TDQS

A4.7/5.0

Behavior4/5

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

The description adds behavioral details beyond the readOnlyHint and openWorldHint annotations: it states the tool is 'authoritative', explains that restricted venues are never returned, and instructs treating it as current coverage rather than memory. This gives useful context about data completeness and licensing constraints.

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 well-structured with clear USE WHEN/USE SOMETHING ELSE markers. It is concise but information-dense, covering output fields, use cases, and an important caveat in about 100 words. Every sentence adds value.

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?

For a simple list tool with an output schema and zero params, the description fully covers purpose, usage, and key behavioral caveats. It leaves no obvious gap for an agent to select or invoke the tool incorrectly.

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?

With zero parameters, the description does not need to elaborate on params. The baseline for 0 params is 4, and the description appropriately focuses on output and usage rather than misleading parameter details.

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 lists venues published by SkipSeek with specific fields (slug, display name, kind, reliability weight, homepage). It distinguishes itself from siblings like list_markets and list_discrepancies by focusing on the venue set rather than individual 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 provides USE WHEN scenarios: needing a valid platform slug for list_markets, or answering questions about venue coverage. Also gives a clear exclusion: USE SOMETHING ELSE for specific market questions, directing to alternatives. This is exemplary guidance.

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

A4.7/5.0

Disambiguation5/5

Every tool targets a distinct resource and action: create_forecast is the only write tool, get_* reads single entities (market probabilities, history, research, forecaster record), and list_* scans collections (markets, movers, discrepancies, platforms). No two tools overlap in purpose, and the three market reads are clearly separated as current vs. historical vs. qualitative.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: get_ for single-item reads, list_ for collection reads, and create_ for the sole write operation. There are no mixed conventions or vague verbs.

Tool Count5/5

9 tools is well-scoped for a prediction-market server: one deliberate write action, four single-market/forecaster reads, and four collection-browsing tools. Nothing feels redundant, and the count is right in the sweet spot for the domain.

Completeness4/5

The server covers the core workflow: discover markets (list_markets), analyze current price/history/research (get_market_*), detect cross-venue disagreement and movement (list_discrepancies, list_movers, list_platforms), and commit a forecast (create_forecast) with auditing via get_forecaster_record. A minor gap is the lack of a tool to fetch a single forecast's details after submission, though the returned URL and aggregate record mitigate this.

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