Versium REACH

Demographic Append Mcp Tool

demographic_append

Read-onlyIdempotent

Append consumer marketing records with household and lifestyle insights for audience segmentation, across demographic, financial, lifestyle, political, or full demographic categories

**Tips for Best Results:**
- Request only the insight categories you need to keep responses focused
- Combine name, location, and digital identifiers (email or phone) for higher match confidence
- Use `cfg_required` or `rcfg_require_value` to enforce must-have attributes in the response

Input Schema

TableJSON Schema

Name	Required	Description	Default
`zip`	No	5 digit ZIP code
`city`	No	City name
`last`	No	Person's last name
`email`	No	Email address to enrich
`first`	No	Person's first name
`phone`	No	10 digit phone number without formatting
`state`	No	Two-character state code (US)
`address`	No	Street address (line 1)
`outputs`	Yes	Insight categories to return. Choose at least one category
`address_2`	No	Apartment or unit number
`match_type`	No	Match at household (hhld) or individual (indiv) level	hhld
`cfg_maxrecs`	No	Maximum number of records to return
`cfg_required`	No	Comma or semi-colon separated response fields that must be populated for a record to be returned
`rcfg_require_value`	No	Field/value requirements in "Field=Value" format. Example: ["Language=Spanish"]

Output Schema

TableJSON Schema

Name	Required	Description
`results`	No	Records returned by the Operations API. Record fields pass through unchanged.
`query_id`	No
`warnings`	No
`input_query`	No
`num_matches`	No	Number of matching queries.
`num_results`	No	Number of records returned.
`match_counts`	No	Sparse match counts. An absent key means zero.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedAug 20, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "input_query": {
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "match_counts": {
+      "description": "Sparse match counts. An absent key means zero.",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "num_matches": {
+      "description": "Number of matching queries.",
+      "type": [
+        "integer",
+        "null"
+      ]
+    },
+    "num_results": {
+      "description": "Number of records returned.",
+      "type": [
+        "integer",
+        "null"
+      ]
+    },
+    "query_id": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "results": {
+      "description": "Records returned by the Operations API. Record fields pass through unchanged.",
+      "items": {
+        "type": "object"
+      },
+      "type": [
+        "array",
+        "null"
+      ]
+    },
+    "warnings": {
+      "items": {
+        "type": [
+          "string",
+          "integer",
+          "number",
+          "boolean",
+          "object",
+          "array",
+          "null"
+        ]
+      },
+      "type": [
+        "array",
+        "null"
+      ]
+    }
+  },
+  "type": "object"
+}

First observedAug 19, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating a non-destructive read. The description adds value by explaining that match confidence improves with combined identifiers and that required-attribute filtering is possible, but it doesn't disclose additional behavior like rate limits or response variability. No contradictions found.

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 compact and well-structured, with the main purpose stated up front and a bulleted 'Tips for Best Results' section. It avoids redundancy with the schema and each sentence adds practical guidance. Slightly more verbose than necessary but still efficient.

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?

Given 14 parameters, a fully described schema, and an output schema, the description provides enough operational context: it lists available categories, explains match confidence factors, and points to filtering mechanisms. It does not explain response format, but the output schema covers that. Minor gaps like pagination are addressed by cfg_maxrecs in the schema.

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%, so the schema already documents all parameters. The description adds context for cfg_required and rcfg_require_value in the tips, and clarifies that combining identifiers boosts match confidence, which is useful but not extensive. Baseline for high coverage is 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool appends household and lifestyle insights to consumer marketing records for audience segmentation, listing the categories (demographic, financial, lifestyle, political, full demographic). It implies a consumer focus, distinguishing it from business-oriented siblings like b2b2c_append or firmographic_append, though it doesn't explicitly name alternatives.

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?

Provides actionable best-practice tips: limiting categories, combining identifiers for match confidence, and using cfg_required/rcfg_require_value to enforce attributes. However, it does not explicitly state when to choose this tool over sibling append tools (e.g., contact_append or c2b_append), leaving some inference required.

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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