OneQAZ Trading Intelligence

get_signal_calibration

Read-onlyIdempotent

Purpose: Reliability diagram data for Level-1 signal confidence — realized hit rate per confidence bucket ([0.5,0.6) ... [0.9,1.0]) with ECE summary. Lets an agent verify whether a 0.9-confidence signal actually hits ~90%. Triggers (casual questions too): "is your confidence calibrated?", "confidence 0.9 믿어도 돼?", "시그널 확신도 실제 적중률 보여줘", "how reliable are signal confidences?". When to call: before trusting get_signals confidence values as probabilities. Prerequisites: none. Next steps: get_prediction_accuracy (macro-layer skill), get_signals. Caveats: snapshot is daily; observation window ≈ signals table retention (~2 weeks); n is nominal (correlated trials — see meta.sample_caveat).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`variant`	No	"v1" (raw heuristic confidence, default) or "v2" (outcome-based shadow confidence — RCA C2, accumulating since 2026-07-21)	v1
`interval`	No	Optional candle interval filter (e.g. 15m, 30m, 240m, 1d)
`market_id`	No	Optional filter (crypto \| kr_stock \| us_stock)

Output Schema

TableJSON Schema

Name	Required	Description
`full_data`	No
`timestamp`	Yes	RFC3339 UTC, server build time
`disclaimer`	Yes	Canonical compliance disclaimer (always present)
`request_id`	Yes	32-hex per-response correlation id
`is_real_money`	No
`data_classification`	No
`is_investment_advice`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 23, 2026

addedOutput schema / properties

Added value: +{
+  "data_classification": {
+    "anyOf": [
+      {
+        "const": "research_information_only",
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  },
+  "disclaimer": {
+    "description": "Canonical compliance disclaimer (always present)",
+    "type": "string"
+  },
+  "full_data": {
+    "anyOf": [
+      {
+        "additionalProperties": true,
+        "description": "`full_data` for get_signal_calibration — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "markets": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "meta": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "snapshot_day": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "variant": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          }
+        },
+        "type": "object"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  },
+  "is_investment_advice": {
+    "anyOf": [
+      {
+        "const": false,
+        "type": "boolean"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  },
+  "is_real_money": {
+    "anyOf": [
+      {
+        "const": false,
+        "type": "boolean"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null
+  },
+  "request_id": {
+    "description": "32-hex per-response correlation id",
+    "type": "string"
+  },
+  "timestamp": {
+    "description": "RFC3339 UTC, server build time",
+    "type": "string"
+  }
+}

addedOutput schema / required

Added value: +[
+  "disclaimer",
+  "request_id",
+  "timestamp"
+]

Changed7 schema fields changedSep 22, 2026

addedInput schema / properties / interval / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]

addedInput schema / properties / interval / description

Added value: +"Optional candle interval filter (e.g. 15m, 30m, 240m, 1d)"

removedInput schema / properties / interval / type
```
Removed value: -"string"
```

addedInput schema / properties / market_id / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]

addedInput schema / properties / market_id / description

Added value: +"Optional filter (crypto | kr_stock | us_stock)"

removedInput schema / properties / market_id / type
```
Removed value: -"string"
```

addedInput schema / properties / variant / description

Added value: +"\"v1\" (raw heuristic confidence, default) or \"v2\" (outcome-based shadow confidence — RCA C2, accumulating since 2026-07-21)"

Changed1 schema field changedJul 21, 2026
- addedInput schema / properties / variant
```
Added value: +{
+  "default": "v1",
+  "type": "string"
+}
```
AddedJul 20, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds important behavioral context: daily snapshot, ~2-week observation window, and nominal n with correlated trials caveat. This goes beyond annotations and helps the agent understand data freshness and limitations.

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?

The description is well-structured with headers (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats) and front-loaded with the purpose. The trigger examples are somewhat redundant but add value for query recognition. Slightly long but every section earns its place.

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?

With an output schema present, return values need no explanation. The description covers purpose, usage context, prerequisites, next steps, and data caveats. It is complete for an agent to decide when and how to call it correctly.

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 all parameters are fully documented there. The description does not add any parameter-specific meaning beyond what the schema provides, so baseline 3 is appropriate.

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 purpose: returns reliability diagram data for Level-1 signal confidence with hit rate per bucket and ECE summary. It clearly differentiates from siblings like get_structure_calibration and get_signals by focusing on confidence calibration verification.

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

Usage Guidelines5/5

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

Explicitly says 'When to call: before trusting get_signals confidence values as probabilities.' Also provides trigger examples and next steps, giving clear context for when to use this tool versus alternatives.

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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