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gov-data-mcp

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parcel-owner-lookup

Resolve US street addresses to parcel ID, owner name, mailing address, and assessed value from official assessor rolls. Supports multiple states and counties.

Instructions

Parcel Owner Lookup — Address to Owner & Assessor Record. Address to parcel ID, owner name, mailing address & assessed value from official assessor rolls: Chicago, Philadelphia, NYC + NC, NY State, WI, CO, MN, AR, MA, CT, VT statewide, Phoenix, Houston, Cleveland, Nashville & DC. Census fallback elsewhere. $0.02/lookup. Reads live from the official government source. COST AND SIDE EFFECTS: read-only with respect to the government source — it never writes to any external system — but each call starts a metered run on YOUR Apify account, billed $0.02 per Result ($20 per 1,000). Lower on paid Apify plans, down to $6.00 per 1,000. Nothing is charged when a run fails. Store page: https://apify.com/malonestar/parcel-owner-lookup

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressesYesUS street addresses to resolve to a parcel + owner record, one per line (e.g. '1060 W Addison St, Chicago, IL'). Matched against the Cook County IL (Chicago), Philadelphia PA and New York City rolls, plus (v1.1) statewide rolls for North Carolina, New York State, Wisconsin, Colorado, Minnesota (opt-in counties), Arkansas, Massachusetts, Connecticut and Vermont and county rolls for Maricopa AZ (Phoenix), Harris TX (Houston), Cuyahoga OH (Cleveland), Nashville TN and Washington DC - routed by the Census-geocoded point's county. Addresses elsewhere fall back to Census geocoding (lat/lon only). Every input address yields exactly one output row; see lookup_status on each row. Example: ["1060 W Addison St, Chicago, IL","1234 Market St, Philadelphia, PA","350 5th Ave, New York, NY"].
maxResultsNoMaximum number of addresses to process (one output row per address). Extra addresses beyond this cap are skipped. Example: 10. Applied by default if omitted: 100.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.2.0
    • changedInput schema / properties / addresses / description
      Previous value: -"US street addresses to resolve to a parcel + owner record, one per line (e.g. '1060 W Addison St, Chicago, IL'). v1 matches against the Cook County IL (Chicago), Philadelphia PA, and New York City assessment rolls; addresses outside those areas fall back to Census geocoding (lat/lon only). Every input address always yields exactly one output row — unmatched addresses come back with match_confidence 'none'. Example: [\"1060 W Addison St, Chicago, IL\",\"1234 Market St, Philadelphia, PA\",\"350 5th Ave, New York, NY\"]."New value: +"US street addresses to resolve to a parcel + owner record, one per line (e.g. '1060 W Addison St, Chicago, IL'). Matched against the Cook County IL (Chicago), Philadelphia PA and New York City rolls, plus (v1.1) statewide rolls for North Carolina, New York State, Wisconsin, Colorado, Minnesota (opt-in counties), Arkansas, Massachusetts, Connecticut and Vermont and county rolls for Maricopa AZ (Phoenix), Harris TX (Houston), Cuyahoga OH (Cleveland), Nashville TN and Washington DC - routed by the Census-geocoded point's county. Addresses elsewhere fall back to Census geocoding (lat/lon only). Every input address yields exactly one output row; see lookup_status on each row. Example: [\"1060 W Addison St, Chicago, IL\",\"1234 Market St, Philadelphia, PA\",\"350 5th Ave, New York, NY\"]."
  2. First observedv1.0.2

TDQS

A4.2/5.0
Behavior5/5

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

Annotations only flag the generic profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false); the description explains why — each call starts a metered Apify run billed $0.02/result, with nothing charged on failure. It also discloses the read-only relationship to the government source and the one-row-per-address contract, which is materially useful beyond the annotations.

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 is front-loaded, but the entry is bloated: a title restatement, an extensive jurisdiction list that repeats the schema parameter description verbatim, pricing marketing ('Low on paid Apify plans'), and a store-page URL. Several sentences do not earn their place for an agent deciding whether to call the tool.

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?

There is no output schema, and the description compensates by naming the returned fields, the one-row-per-address guarantee, and the lookup_status signal, plus cost/side-effect disclosure. A brief note on absent-coverage output shape would close the remaining gap, but the essentials are present for a 2-parameter tool.

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%, and the schema already documents addresses (format, examples, routing/fallback) and maxResults (cap, default 100). The description adds no syntax or semantics beyond that, so the baseline of 3 applies; its coverage list largely duplicates the schema text.

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 names a specific transformation — US street address → parcel ID, owner name, mailing address, assessed value — and identifies the authoritative source (official assessor rolls). It reads as a distinct lookup capability against the sibling gov-data tools, so an agent can select it without opening the schema.

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 clear context for use: which jurisdictions are covered by direct assessor lookup, which fall back to Census geocoding (lat/lon only), and that every input yields exactly one row. There is no explicit 'do not use this when…' or named alternative tool, so it stops short of full routing 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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