Versium REACH

Demographic Append Mcp Tool

demographic_append

Read-onlyIdempotent

Append consumer records with household and lifestyle insights 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.

TDQS

A4.2/5.0

Behavior3/5

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

Annotations already communicate readOnlyHint, idempotentHint, and non-destructive behavior, so the description does not need to restate safety traits. It adds context about matching confidence and response focus, but it does not disclose details like fallback behavior when no match is found or how output is structured.

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

Conciseness5/5

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

The description is compact, action-first, and structured into a main purpose statement followed by a short tips section. Every sentence earns its place, and the formatting makes the guidance easy to skim.

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 that the tool has 14 parameters and an output schema, the description provides sufficient practical context for selection and invocation. It does not explicitly compare to sibling tools, but it covers input strategy, required fields, output category selection, and parameter controls, which is strong coverage.

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 description coverage is 100%, so each parameter already has a description. The tool description adds meaningful parameter-level context by advising identifier combinations and by explaining when to use cfg_required and rcfg_require_value, which goes beyond the schema definitions.

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 states a specific action: append consumer records with household and lifestyle insights across defined categories (demographic, financial, lifestyle, political, full demographic). This clearly identifies the tool's purpose and distinguishes it from sibling tools like firmographic_append or contact_append.

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 provides actionable usage guidance: request only needed categories, combine identifiers for higher match confidence, and use cfg_required or rcfg_require_value to enforce must-have attributes. It does not explicitly discuss alternatives among siblings, but it gives clear context for effective use.

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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TDQS

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct resource and action: list generation (B2B vs B2C), enrichment (contact, demographic, firmographic, C2B, IP-to-domain), project/list management, job lifecycle, and validation. No two tools appear to do the same thing, so an agent can reliably select the correct one.

Naming Consistency5/5

All tool names follow the verb_noun pattern consistently, using verbs like list, show, create, preview, search, validate, append, and estimate. Even the segmented append tools (c2b_append, contact_append, demographic_append, firmographic_append, ip_to_domain_append) follow the same pattern with clear noun modifiers. No mixed conventions or vague verbs.

Tool Count4/5

With 19 tools, this is on the higher side but still well-scoped for a data enrichment and list-building platform. The count covers distinct functional areas (audience estimation, enrichment, job/project management, list inspection, documentation) without unnecessary bloat. It feels slightly heavy but each tool earns its place.

Completeness4/5

The tool surface covers the core workflows: estimating audiences, creating and monitoring jobs, managing projects and lists, and enriching records with various data types. Minor gaps exist, such as no explicit delete/update operations for projects or lists, and no direct file download for list contents (though preview_list and show_list provide partial coverage). These are non-blocking gaps that agents can work around.

Resources

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