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Das-rebel

Marketic

by Das-rebel

track_signal

Record a signal prediction to track prediction accuracy over time. Later resolve the signal to calculate Brier score and improve calibration.

Instructions

Record a signal prediction for later calibration tracking. Once tracked, you can call resolve_signal when the outcome is known to measure prediction accuracy (Brier score). Used to build calibration over time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
titleYes
sourceYes
topicsNo
metadataNo
signal_typeYes
engagement_scoreYes
Install Server

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description bears the full burden of behavioral disclosure. It notes that the tool records a prediction for later calibration and links to resolve_signal, but it does not mention side effects, idempotency, data retention, or any other behavioral traits. For a simple record operation, it is adequate but lacks depth.

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?

Two sentences, both front-loaded: the first states the core action and purpose, the second gives the follow-up workflow. No extraneous content—every sentence earns its place.

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

Completeness2/5

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

Given 7 parameters (5 required), no schema descriptions, no annotations, and no output schema, the description is far too sparse. It does not explain expected value formats (e.g., what a valid signal_type is), constraints on engagement_score, or how to handle topics/metadata. The workflow link helps but cannot compensate for this missing operational context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate by explaining parameter meanings. It does not describe title, source, signal_type, url, engagement_score, topics, or metadata at all. The only hint is the general phrase 'signal prediction,' which provides no operational guidance for filling parameters.

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 action ('Record a signal prediction') and a clear resource (signal prediction), and explains its role in calibration tracking. It also distinguishes itself from a sibling by referencing resolve_signal for outcome measurement, making the purpose unambiguous.

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?

It clearly frames the workflow: track a signal now, then use resolve_signal later to compute Brier score. This implies the tool is for initial recording, but it does not explicitly mention alternatives like collect_signals or state when not to use it, leaving some room for inference.

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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