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

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

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

A4.2/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds transparency by specifying the exact return fields, including a note that 'Misreads are reported,' which indicates the tool includes errors in its output. This goes beyond the annotation baseline and provides useful 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately long but well-organized, starting with the core purpose, then listing return fields, then optional scoping, and ending with usage guidance. It is informative without being rambling, and the key information is front-loaded.

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?

For a read-only query tool with an output schema, the description covers the purpose, when to use it, what it returns, and optional parameters. It also mentions the reporting of misreads, which is an important nuance. There are no obvious gaps that would prevent an agent from calling 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 coverage is 100%, so all three parameters have descriptions. The description adds a bit of context by mentioning optional scoping and the concept of a 'constituent set' for corridors, but it doesn't substantially extend what the schema already documents. 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?

The description clearly states the tool's purpose: returning the observatory's public calibration scorecard, which aggregates historical forecasting accuracy metrics. It distinguishes itself from sibling tools by focusing on accuracy data rather than entity descriptions or listings, and explicitly names the types of metrics returned.

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 includes a clear 'Use when' clause: when assessing Zoning Signal's historical forecasting accuracy before citing a prediction. It also notes optional scoping to jurisdiction or corridor. It doesn't explicitly list alternatives or exclusions, but the use case is well-defined and distinct from siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Zoning Signal tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?