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WillHsiaoNYC

NYC Open Data Capital Projects MCP Server

by WillHsiaoNYC
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Python
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list_categories

Read-onlyIdempotent

Retrieve program and facility categories with budget-line counts and total budgets to choose a valid category filter for project ranking queries.

Instructions

Program/facility categories (Library, Parks & Recreation, Sewer & Water, …) with budget-line counts and total budget. Use a category name as the category filter on rank_projects. Categories are derived from ten_year_plan_category + sponsor_agency + fms-id prefix — NOT managing_agency or project name.

Interpretation rules:

  • [categories] Classify with the curated category_dim, not project-name searches: specific ten-year labels/FMS prefixes precede sponsor routing, then generic facility keywords and Other. Categories key on (managing_agency, fms_id); institution owner rules can use all-history ownership. Schedule category_scope='current' uses each PID's current funding links; 'all_history' includes former links. A PID can count in multiple categories, so schedule category counts are non-additive; each budget line has one category.

  • [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.

  • [period_basis] Reporting-period basis: state the returned period for every count, total or ranking and both periods for comparisons. Period aggregates default to the latest complete snapshot, which may precede a partially published newer period. fms_location, fms_sponsor and lifetime_budget_variance are all-history dimensions without reporting_period: use them for enrichment or lifetime figures, never as a single period's inventory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNo
categoriesYes
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: +{
+  "$defs": {
+    "InterpretationRule": {
+      "properties": {
+        "id": {
+          "description": "Stable identifier of the domain rule.",
+          "title": "Id",
+          "type": "string"
+        },
+        "text": {
+          "description": "Guidance for interpreting and reporting this tool's result.",
+          "title": "Text",
+          "type": "string"
+        }
+      },
+      "required": [
+        "id",
+        "text"
+      ],
+      "title": "InterpretationRule",
+      "type": "object"
+    }
+  },
+  "additionalProperties": true,
+  "properties": {
+    "categories": {
+      "items": {
+        "additionalProperties": true,
+        "type": "object"
+      },
+      "title": "Categories",
+      "type": "array"
+    },
+    "interpretation_rules": {
+      "items": {
+        "$ref": "#/$defs/InterpretationRule"
+      },
+      "title": "Interpretation Rules",
+      "type": "array"
+    },
+    "period": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null,
+      "title": "Period"
+    },
+    "provenance": {
+      "additionalProperties": true,
+      "title": "Provenance",
+      "type": "object"
+    }
+  },
+  "required": [
+    "provenance",
+    "interpretation_rules",
+    "categories"
+  ],
+  "title": "CategoriesResult",
+  "type": "object"
+}
  2. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With readOnlyHint/idempotentHint already covering the safety profile, the description still adds real behavioral context: categories are derived from specific fields, schedule counts are non-additive (a PID can appear in multiple categories), and rows must be deduplicated at the requested grain. These are non-obvious traits an agent could not infer from annotations or schema.

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?

Purpose and routing are front-loaded, which is good. However, the dense interpretation-rules block runs long, mixing grain, period-basis, and category-derivation guidance into one tool entry; some of it reads as dataset-wide rules that could sit elsewhere, diluting scannability.

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?

An output schema exists, so return values need not be explained, and the description compensates by covering grain, category derivation, and period-basis caveats. It is largely complete for a no-param enumerator, though the volume of interpretive rules suggests some complexity is pushed onto the reader rather than resolved.

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?

The tool takes zero parameters, so the schema carries no parameter semantics and the baseline is 4. The description instead spends its detail on grain and filtering conventions, which is appropriate for a parameterless enumerator.

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 opening names a specific resource (program/facility categories) with concrete examples and states the returned data (budget-line counts and total budget). The routing line 'Use a category name as the category filter on rank_projects' cleanly separates this from sibling lookup tools.

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?

It gives explicit context by naming the downstream consumer ('Use a category name as the category filter on rank_projects'), which implies when this tool is the right first step. It does not explicitly exclude alternatives like describe_table or list_agencies, so it stops short of full when-not guidance.

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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