Recorded outcomes of the live pipeline's own decisions: hit rates, score-band tables, accepted-vs-rejected edge, gate near-miss regret. Aggregated engine telemetry — percent moves and counts only.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry behavioral disclosure. It describes the output as aggregated engine telemetry (percent moves and counts), implying a read-only report. However, it does not explicitly state it is non-destructive or mention any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, clear and directly informative. Slightly verbose with the list of metrics but no wasted words. Front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless report tool, the description adequately covers the return value and purpose. Missing details like update frequency or real-time nature, but acceptable given low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist in the schema, so baseline is 4. The description explains what the tool returns without needing parameter details, which is appropriate given zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool records outcomes of live pipeline decisions, listing specific metrics like hit rates and score-band tables. It distinguishes itself from sibling tools like calibration or explanation reports by focusing on aggregated telemetry.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. The description implies it's for aggregated telemetry but does not state when not to use it or mention sibling tools for comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: calibration telemetry, signal evaluation, explanation, feature attribution, catalog listing, and outcome reporting. No two tools overlap in functionality.
All tools share the 'alphalabs_' prefix and follow a verb_noun or noun_noun pattern. The mix of verb_noun (evaluate_signal, explain_decision, get_catalog) and noun_noun (calibration_report, feature_attribution, outcome_report) is a minor inconsistency, but still predictable and readable.
With 6 tools, the server is neither too sparse nor too heavy. Each tool covers a distinct aspect of the intelligence pipeline, making the set well-scoped for its purpose.
The tool surface covers the full lifecycle of signal evaluation: evaluation, explanation, feature analysis, calibration reporting, and outcome tracking. Missing features like market data access are explicitly out of scope, so no dead ends remain.