skipseek

Research briefing for a market

get_market_research

Read-only

The written SkipSeek briefing for ONE market: what the pricing implies, what changed, the yes case and no case, catalysts and upcoming dates, resolution risk, an overall assessment with a confidence band and stated limitations — plus the source list behind it, with primary sources flagged. USE WHEN you need reasoning and citations rather than numbers, e.g. "why is this priced at 34%" or "what would move this". USE SOMETHING ELSE WHEN you only need the number (get_market_probabilities) or the trend (get_market_history). Read-only. Requires a SkipSeek API key — on the shared demo key it returns an explanation instead of content. Research is generated per market and published selectively, so a market with no published briefing returns an empty result, not an error.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`market`	Yes	Market slug, exactly as returned in the `slug` field by list_markets, list_discrepancies or list_movers (e.g. "fed-cuts-rates-by-september"). Opaque — do not construct or guess one; a slug that does not exist returns a not-found message rather than data.

Output Schema

TableJSON Schema

Name	Required	Description
`tier`	No
`market`	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
`sources`	No
`research`	No
`attribution`	No
`generated_at`	No
`requests_remaining_today`	No

TDQS

A5/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses the API key requirement, the demo-key behavior ('returns an explanation instead of content'), and the empty-result-for-unpublished-market behavior. It also warns that a nonexistent slug returns a not-found message rather than data, covering boundary cases well.

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 front-loaded with the briefing contents and use cases, then exclusions, then operational notes. Every sentence carries distinct information with no fluff, and the use-case guidance is separated into clear directive phrases.

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?

Given the single-parameter schema, existing output schema, and annotations, the description covers all necessary context: expected input, auth, result content, edge cases, and exclusions. It leaves no ambiguity about what the tool returns or when to call it.

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

Parameters5/5

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

The parameter description in the schema is already thorough, and the tool description adds crucial guidance: the slug must come exactly from sibling list endpoints, it is opaque and must not be constructed, and a bad slug yields a not-found message. This fully compensates and enriches the schema's meaning.

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 states exactly what the tool returns: a written briefing for one market including pricing implications, yes/no cases, catalysts, risk, confidence band, limitations, and a source list. It explicitly contrasts with sibling tools get_market_probabilities and get_market_history, making the tool's unique purpose unmistakable.

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?

The description gives explicit when-to-use guidance ('USE WHEN you need reasoning and citations rather than numbers') and when-not-to-use guidance with named alternatives ('USE SOMETHING ELSE WHEN you only need the number...'). It also informs users about empty results for unpublished briefings, setting accurate expectations.

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

All skipseek tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?