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fair draw

fair_draw
Read-only

Use this after fair_commit, once every player has seen the server_seed_hash: give your client_seed and get uniform random integers in [min, max]. The server reveals its seed with the draw; a commitment draws once (a second call returns the original draw). Algorithm swoge-hmac-sha256-v1: HMAC-SHA256(server_seed, client_seed:counter) read 4 bytes at a time, rejection sampling, no bias; the answer carries code that redoes it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxNolargest value (default 100)
minNosmallest value (default 1)
countNohow many numbers (default 1)
quoteNotrue: return the price of this call without running or paying for it
client_seedYesyour seed, 1 to 128 printable ASCII characters (pick it after seeing the server_seed_hash)
commitment_idYesthe id fair_commit returned

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the readOnly/openWorld annotations: discloses that the server seed is revealed with the draw, that a commitment draws exactly once (second call returns the original draw, i.e. idempotent), that the call is paid, that the response carries replayable verification code, and spells out the HMAC-SHA256/rejection-sampling algorithm. These are exactly the behavioral traits an agent needs and that the annotations do not supply.

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?

Front-loads the trigger condition ('Use this after fair_commit...') so the routing information comes first. The second sentence is dense with algorithm, idempotency, and verifiability details, but each clause earns its place for a fairness-critical tool.

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?

No output schema exists, and the description compensates by describing the return (uniform integers in [min, max], revealed server seed, embedded verification code). With 100% schema parameter coverage and annotations handling the safety profile, the definition is complete enough to call correctly and interpret the result.

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?

Schema coverage is 100% so the baseline is 3, but the description adds real meaning: it explains that client_seed must be chosen after seeing the server_seed_hash, and that min/max bound a uniform, bias-free result. It adds relatively little beyond the schema for quote or count.

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?

States a specific verb+resource (draw uniform random integers in [min, max] from a commitment) and explicitly positions itself relative to fair_commit and fair_verify in the workflow. An agent can distinguish it from fair_commit and fair_verify without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Names the prerequisite sibling and the exact timing condition: 'after fair_commit, once every player has seen the server_seed_hash'. It also notes the quote escape hatch for pricing without paying. It does not, however, name fair_verify as the alternative for auditing the result, so the alternative side of the guidance is only implied.

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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