skipseek

Market probability history

get_market_history

Read-only

Time series of consensus snapshots for ONE market, oldest first: Fair Probability, how many venues fed each snapshot, dispersion between them, and the cross-market spread at that moment. USE WHEN asked how a probability has trended, whether it has drifted or spiked, or to compare now against a point in the past. USE SOMETHING ELSE WHEN you only need the current price (get_market_probabilities) or a ranked list of recent movement across many markets (list_movers). Read-only. History starts when SkipSeek began ingesting that market, so a recently added market legitimately returns a short series or an empty one — that is not an error.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum snapshots to return, keeping the most recent and still ordered oldest-first. Defaults to 100. Demo callers get at most 5.
`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
`attribution`	No
`generated_at`	No
`consensus_history`	No	OLDEST FIRST.
`requests_remaining_today`	No

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description adds critical behavioral context: history starts when SkipSeek began ingesting the market, so a short or empty series is legitimate and not an error. It also clarifies ordering (oldest first) and the semantic meaning of the snapshots. No contradiction with annotations.

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

Conciseness4/5

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

The description is moderately long but every sentence earns its place: it front-loads the core purpose, then usage guidance, then an important caveat. It is well-structured with clear transitions, though slightly verbose compared to the most minimal examples. A 4 reflects strong structure with no wasted 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?

Given the output schema exists, the description need not explain return values. It covers purpose, usage timing, alternatives, data semantics, and edge-case behavior (empty series). This is complete for a tool of this complexity, especially with the rich sibling differentiation.

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?

Schema description coverage is 100%, so the baseline is 3. The description does not add new parameter-level meaning beyond what the schema already provides (e.g., limit behavior, market slug sourcing). It does mention 'ONE market' which reinforces the market parameter's singular nature, but this is already clear from the schema. Baseline 3 is appropriate.

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+resource ('Time series of consensus snapshots for ONE market') and details the exact contents (Fair Probability, venue count, dispersion, cross-market spread). It clearly distinguishes itself from sibling tools like get_market_probabilities and list_movers by naming them in the usage guidance.

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 'USE WHEN' conditions (trend, drift/spike, compare to past) and explicit 'USE SOMETHING ELSE WHEN' conditions with named alternatives (get_market_probabilities for current price, list_movers for ranked movement). This is textbook usage guidance.

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?