Zoning Signal

Get Track Record

get_track_record

Read-onlyIdempotent

Return the observatory's public calibration scorecard — the aggregate accuracy of past watch-item directional reads, horizon calls, and significance assessments across resolved watches. Returns: total_resolved, directional accuracy (aligned + 0.5 × mixed), horizon accuracy (within / total), significance accuracy (confirmed / total), per-confidence-pip stratification, recent resolutions, and per-jurisdiction breakdown. Optionally scope to a single jurisdiction or corridor's constituent set. Use when an agent or user wants to assess Zoning Signal's historical forecasting accuracy before citing a current prediction. Misreads are reported.

Input Schema

TableJSON Schema

Name	Required	Description
`brief`	No	Optional: scope to a brief slug (e.g., "six-month-board-flip"). Returns the track record for watches linked to a specific named-pattern brief.
`corridor`	No	Optional: scope to a corridor slug (e.g., "us-27-south-lake"). Returns the aggregate track record across the corridor's constituent places.
`jurisdiction`	No	Optional: scope to a single place slug (e.g., "leesburg-florida") for that city's track record only. Use list_cities to discover available slugs.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`scope`	No
`recent`	No
`horizon`	No
`pending`	No
`obsolete`	No
`surfaces`	No
`directional`	No
`significance`	No
`by_confidence`	No
`total_resolved`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedMay 9, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "by_confidence": {
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "directional": {
+      "properties": {
+        "accuracy_pct": {
+          "type": "number"
+        },
+        "aligned": {
+          "type": "number"
+        },
+        "misread": {
+          "type": "number"
+        },
+        "mixed": {
+          "type": "number"
+        },
+        "total": {
+          "type": "number"
+        },
+        "unforeseen": {
+          "type": "number"
+        }
+      },
+      "type": "object"
+    },
+    "horizon": {
+      "properties": {
+        "accuracy_pct": {
+          "type": "number"
+        },
+        "early": {
+          "type": "number"
+        },
+        "late": {
+          "type": "number"
+        },
+        "total": {
+          "type": "number"
+        },
+        "within": {
+          "type": "number"
+        }
+      },
+      "type": "object"
+    },
+    "obsolete": {
+      "type": "number"
+    },
+    "pending": {
+      "type": "number"
+    },
+    "recent": {
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "scope": {
+      "type": "object"
+    },
+    "significance": {
+      "properties": {
+        "accuracy_pct": {
+          "type": "number"
+        },
+        "confirmed": {
+          "type": "number"
+        },
+        "overstated": {
+          "type": "number"
+        },
+        "total": {
+          "type": "number"
+        },
+        "understated": {
+          "type": "number"
+        }
+      },
+      "type": "object"
+    },
+    "surfaces": {
+      "properties": {
+        "list_resolved_watches": {
+          "type": "string"
+        },
+        "public_scorecard": {
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "total_resolved": {
+      "type": "number"
+    }
+  },
+  "type": "object"
+}

AddedMay 8, 2026

TDQS

A4.3/5.0

Behavior4/5

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

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint as safe. The description adds substantial value beyond this by listing in detail what metrics are returned (directional accuracy, horizon accuracy, etc.) and importantly discloses that 'Misreads are reported,' setting expectations that the data includes errors. This is rich behavioral context.

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 front-loaded with the core purpose, then enumerates return fields, then gives usage guidance, and ends with a caveat. Every sentence adds value, and it is appropriately sized for the tool's complexity—informative without being verbose.

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?

The tool has an output schema and 100% schema parameter coverage, yet the description still enriches the context by explaining the return structure and strategic usage. It fully covers the 'what', 'when', and 'what to expect' aspects for an agent, making it self-contained.

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%, with each parameter (brief, corridor, jurisdiction) already described with examples. The description adds minimal extra semantic meaning beyond restating that scoping is optional. The baseline of 3 applies because the schema carries the heavy lifting for parameter explanation.

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 clearly states the tool's function: 'Return the observatory's public calibration scorecard' with specific aggregate accuracy metrics. It distinguishes itself from sibling tools like describe_watch or list_watch_items by focusing on historical forecasting performance rather than individual entities or simple listings.

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 provides a clear usage context: 'Use when an agent or user wants to assess Zoning Signal's historical forecasting accuracy before citing a current prediction.' It does not explicitly name alternatives or exclusion scenarios, but the context is strong and implies when this tool is appropriate.

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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