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NYC Open Data Capital Projects MCP Server

by WillHsiaoNYC
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describe_field

Read-onlyIdempotent

Get field definitions from the NYC Open Data data dictionary to understand column meanings, allowed values, keys, and limitations; filter by field or dataset.

Instructions

Official field definitions (the NYC Open Data data dictionary): description, allowed values, primary/foreign key, limitations, notes. Filter by field (column name or display name) and/or dataset (RAW table name or socrata_id); omit both for the full dictionary.

Interpretation rules:

  • [grain] Schedule questions use PID; budget questions use the BUDGET LINE key (managing_agency, fms_id). The same FMS ID under different holders is distinct lines. Schedule history is PID x reporting_period; budget history is budget line x reporting_period; fiscal-year budgets also key on fiscal_year. The schedule source has no fms_id; budget sources have no pid. The combined source repeats rows across PID-budget links and location splits: deduplicate at the requested entity grain before counting or summing. Never compare budgets using fms_id alone.

  • [lifecycle] Lifecycle: Pre-Design -> Design -> Construction Procurement -> Construction -> Close-out. Schedule progression is reported from Design through Construction. Forecasts and most actual milestones can be suppressed outside those phases; actual_construction_end is the exception and means substantial completion. NULL milestones need not mean missing data. Budget spend%=100 does not prove completion. forecast_past_due is evaluated as of the observation period, not today's date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldNo
datasetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsYes
provenanceYes
interpretation_rulesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.1
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$defs": {
+    "InterpretationRule": {
+      "properties": {
+        "id": {
+          "description": "Stable identifier of the domain rule.",
+          "title": "Id",
+          "type": "string"
+        },
+        "text": {
+          "description": "Guidance for interpreting and reporting this tool's result.",
+          "title": "Text",
+          "type": "string"
+        }
+      },
+      "required": [
+        "id",
+        "text"
+      ],
+      "title": "InterpretationRule",
+      "type": "object"
+    }
+  },
+  "additionalProperties": true,
+  "properties": {
+    "fields": {
+      "items": {
+        "additionalProperties": true,
+        "type": "object"
+      },
+      "title": "Fields",
+      "type": "array"
+    },
+    "interpretation_rules": {
+      "items": {
+        "$ref": "#/$defs/InterpretationRule"
+      },
+      "title": "Interpretation Rules",
+      "type": "array"
+    },
+    "provenance": {
+      "additionalProperties": true,
+      "title": "Provenance",
+      "type": "object"
+    }
+  },
+  "required": [
+    "provenance",
+    "interpretation_rules",
+    "fields"
+  ],
+  "title": "FieldsResult",
+  "type": "object"
+}
  2. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and closed-world, so the safety profile is covered. The description adds output content and confirms both filters are optional, but discloses no behavioral traits an agent needs at call time (result size, pagination, truncation, whether an unknown field name errors or returns empty). The long 'Interpretation rules' block is domain guidance about the underlying data, not behavior of this tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The opening paragraph is tight and front-loaded. But roughly 80% of the text is an 'Interpretation rules' section about data grain and project lifecycle that is tangential to invoking a two-parameter dictionary lookup and reads like reused domain context rather than tool documentation.

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

Completeness4/5

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, and the description covers purpose, both filters, and the no-filter default. An agent can call this correctly from what is given; the surplus interpretation rules neither help nor hurt completeness for this specific tool.

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

Parameters4/5

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

Schema coverage is 0% and neither parameter is documented in the schema, so the description must carry the load — and it does: `field` accepts a column name or display name, `dataset` accepts a RAW table name or socrata_id, and both being omitted returns the full dictionary. That adds real meaning beyond the bare schema, though case-sensitivity and partial-name matching rules remain unstated.

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?

Names a specific verb+resource ('describe_field' -> official field definitions / NYC Open Data data dictionary) and enumerates exactly what is returned: description, allowed values, primary/foreign key, limitations, notes. This is clearly distinguishable from siblings like describe_table and dataset_info, which cover whole tables/datasets rather than individual columns.

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

Usage Guidelines3/5

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

The description states how to scope the call ('Filter by `field` ... and/or `dataset` ... omit both for the full dictionary'), which is genuine usage guidance for the parameters. However it never says when to reach for this tool versus describe_table, dataset_info, or run_sql, so alternative selection is left to 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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