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OpenExp

OpenExp

by anthroos
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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log_outcome

Resolve predictions with observed facts: submit actual_signal and days_to_resolve to record raw outcomes, bypassing interpretation.

Instructions

Resolve a prediction with observed facts: provide actual_signal and days_to_resolve — an interpretation-free record of what happened. Legacy outcome/reward fields are accepted and recorded as data.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prediction_id": "<prediction_id>"
    },
    "name": "log_outcome"
  }
}

Input Schema

NameRequiredDescriptionDefault
notesNoOptional free-text context (e.g. unexpected events)
rewardNo[deprecated — recorded as data, updates nothing]
outcomeNo[deprecated alias for actual_signal — accepted for backward compat]
actual_signalNoWhat was observed — raw fact, no interpretation. Required on the new path.
prediction_idYesID from log_prediction
cause_categoryNo[deprecated, accepted for backward compat]
days_to_resolveNoHow many days from prediction to resolution. Required on the new path.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.0.0
    • changedInput schema / properties / reward / description
      Previous value: -"[deprecated — only used on the legacy Q-update path. Omit on the new path.]"New value: +"[deprecated — recorded as data, updates nothing]"
  2. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that legacy fields are 'accepted and recorded as data' and that the record is 'interpretation-free', which explains what happens to those fields. However, it does not state whether the action is reversible, whether the prediction is permanently marked as resolved, what happens if the prediction_id does not exist, or what the tool returns. These are meaningful gaps for a mutation tool with no annotation support.

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 one concise, front-loaded sentence. It states the core purpose first ('Resolve a prediction with observed facts'), then the required fields, then the legacy behavior. No filler or redundancy; every clause earns its place.

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

Completeness3/5

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

The description provides enough to make a basic call (prediction_id plus actual_signal/days_to_resolve) and explains legacy field handling, but with no output schema and no annotations, it omits important context like return values, error conditions, idempotency, and whether resolution is final. For a tool with 7 parameters and a state-changing action, these gaps make it only partially complete.

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 'interpretation-free record' and the new-path vs legacy-path framing, but the schema already contains these details (e.g., 'Required on the new path' and 'deprecated — recorded as data, updates nothing'). The description does not significantly extend the semantic understanding beyond the schema.

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 states a specific verb ('Resolve') and resource ('a prediction'), and clarifies the tool's role by naming the core fields (actual_signal, days_to_resolve) and framing it as an 'interpretation-free record'. This clearly differentiates it from sibling tools like log_prediction, which creates predictions, and the memory tools, which manage memory.

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 description gives clear context: use this when you have observed facts to resolve a prediction, and instructs the caller to provide actual_signal and days_to_resolve. It does not explicitly mention alternatives or exclusion cases (e.g., 'use log_prediction instead'), but the purpose is specific enough that an agent can infer when to call it. No exclusions are stated, so it falls short of a 5.

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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