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get_world_state

Get a snapshot of real-money probabilities from ~9,700 live prediction markets across geopolitics, economics, tech, and policy. Filter by topic. Returns markdown or JSON.

Instructions

Calibrated world model: ~9,700 live prediction markets distilled into ~800 tokens of real-money probabilities across geopolitics, economics, tech, and policy. Read-only, no auth, no rate limit. Use when you need a compact snapshot of "what the market believes right now"; use get_changes for deltas only, or get_context for the broader bundle including edges and movers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNoComma-separated topic filter. Allowed values: energy, geo, tech, policy, crypto, finance. Omit for all topics.
formatNoOutput format. Default: markdown (human-readable). Use json for programmatic parsing.
Behavior4/5

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

With no annotations provided, the description fully takes on behavioral disclosure. It states 'Read-only, no auth, no rate limit,' clearly indicating safe, unrestricted usage. It also explains the core behavior of distilling markets into tokens. While it doesn't detail return format or size limits, the information is sufficient for a read-only tool of this nature.

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 long. The first sentence concisely sets context and capabilities. The second provides usage guidance and alternatives. Every word earns its place with no redundancy.

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 tool's simplicity (2 optional parameters, no output schema), the description covers all essential aspects: what it does, its source data, security profile, and relation to siblings. It is fully complete for an agent to decide when and how to invoke it.

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 adds no extra detail beyond what the schema already provides for the parameters (focus and format). It does not reiterate or elaborate on parameter usage, so it adds no additional value to parameter semantics.

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 identifies the tool as returning a compact snapshot of market beliefs from live prediction markets, specifying the sources (~9,700 markets), coverage (geopolitics, economics, tech, policy), and output nature (real-money probabilities). It distinguishes itself from siblings by naming get_changes and get_context.

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 explicitly states when to use the tool ('when you need a compact snapshot of what the market believes right now') and provides clear alternatives for other needs ('use get_changes for deltas only, or get_context for the broader bundle'). This is excellent 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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