Skip to main content
Glama

Prediction Markets Quant

by com.predictionmarketspicks
OverviewDiscussionsAdmin

Bayesian Probability Update

bayes_update
Read-only

Update a prior probability with one or more pieces of evidence using Bayes theorem. Given a prior and a list of evidence items (each with P(evidence | true) and P(evidence | false)), returns the posterior probability and the per-step chain. Use for "update my estimate with new information", "posterior probability", "how does this news change the odds".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priorYesPrior probability the hypothesis is true, in % (0–100). Accepts 55, "55%", "55¢", "$0.55", 0.55 or American odds (+120 / -150) — all read as 55%.
evidenceNoOne or more evidence items, applied in order. Each item needs likelihoodIfTrue and likelihoodIfFalse on the 0–100 scale, e.g. [{ "likelihoodIfTrue": 80, "likelihoodIfFalse": 20 }]. A single item may be sent as one object.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / evidence / description
      Previous value: -"One or more evidence items, applied in order."New value: +"One or more evidence items, applied in order. Each item needs likelihoodIfTrue and likelihoodIfFalse on the 0–100 scale, e.g. [{ \"likelihoodIfTrue\": 80, \"likelihoodIfFalse\": 20 }]. A single item may be sent as one object."
    • changedInput schema / required
      Previous value: -[
-  "prior",
-  "evidence"
-]New value: +[
+  "prior"
+]
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true MATERIAL, so the safety profile is covered. The description adds the expected output ('posterior probability and the per-step chain') and the Bayes theorem basis, but it does not go into edge behavior such as rounding, numerical precision, or what happens if evidence is omitted. Given that annotations carry the safety burden, this is adequate but not rich.

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 tight: one action sentence, one input/output sentence, and one usage-intent sentence. There is no tangental detail, and the key operation is front-loaded. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only two parameters holistically documented by the schema and read-only annotations, the description covers the core call context well. It names the return value and the chain, though there is no output schema and the exact shape of the 'per-step chain' is not specified. This is a minor gap rather than a missing critical signal.

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 schema already documents prior, likelihoodIfTrue, likelihoodIfFalse, and label thoroughly. The description only lightly re-states the evidence structure without adding new semantic detail beyond the schema. This matches the baseline for full-coverage schemas.

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 names a specific action—'Update a prior probability with one or more pieces of evidence using Bayes theorem'—and immediately distinguishes the operation from sibling calculators by focusing on posterior updating and per-step chains. It also provides concrete user-intent phrases like 'posterior probability' and 'how does this news change the odds,' which makes the tool unambiguous to an agent.

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?

The 'Use for...' line gives clear natural-language triggers that signal when this tool is appropriate, which is strong usage guidance. It does not explicitly state when not to use it or name alternatives like convert_probability, but the context and examples are sufficient to steer an agent toward correct selection.

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

All Prediction Markets Quant toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources