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Biggest 24-hour movers

list_movers
Read-only

Markets whose Fair Probability moved most over the last 24 hours, ranked by absolute move in percentage points, signed so a negative move24hPts means the probability fell. Only markets with a comparable snapshot from ~24h ago appear. USE WHEN asked what changed, what is trending, or which forecasts repriced today. USE SOMETHING ELSE WHEN you want the full path of one market rather than a single 24h delta (get_market_history), or disagreement between venues rather than change over time (list_discrepancies). Read-only. Returns an empty movers array, not an error, when nothing has a 24h comparison yet — that is normal shortly after a market is first ingested.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return, largest absolute move first. Defaults to 20. Demo callers get at most 5.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNo
moversNoRanked by ABSOLUTE move, but `move24hPts` stays signed - read the sign before saying a probability rose.
noticeNoSet 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.
sourceNo
attributionNo
generated_atNo
requests_remaining_todayNo

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark readOnlyHint=true, but the description adds behavioral context beyond that: signed move values, requirement for a comparable 24h snapshot, ranking by absolute move, and the empty `movers` array return instead of an error. These details manage agent expectations and prevent misinterpretation.

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 compact but structured: definition, edge case, usage guidance, and behavioral note. Every sentence adds value, with no repetition or filler. The length is justified by the clarity it provides.

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 presence of an output schema, the description does not need to re-explain return fields. It covers the empty-array condition, the snapshot requirement, and clearly delineates alternatives. The tool is fully contextualized for an AI agent without needing additional information.

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 coverage for the single `limit` parameter is 100%, so the schema fully explains it. The description mentions ranking and the delta field but does not add new semantic detail for the parameter itself. Baseline 3 is appropriate when the schema does the heavy lifting.

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 markets whose Fair Probability moved most over the last 24 hours, ranked by absolute move in percentage points. It uses a specific verb ('list') + resource ('markets') + scope ('24-hour movers'), and it distinguishes itself from siblings by referencing get_market_history and list_discrepancies as alternatives.

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?

Explicit 'USE WHEN' guidance is given for trending/reprized questions, and 'USE SOMETHING ELSE WHEN' explicitly names alternatives (get_market_history for full paths, list_discrepancies for venue disagreement). This is exactly the level of guidance needed for tool selection.

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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