Versium REACH

B2 B2 C Append Mcp Tool

b2b2c_append

Read-onlyIdempotent

Enrich a business contact with their full business-to-consumer profile: with a LinkedIn URL or a name + city/state, retrieve business email plus consumer mobile phone, consumer email, and consumer address data in a single match.
Use this tool when users ask for 'B2B2C', 'b2b2c', or 'Business to Business to Consumer' data.

**Tips for Best Results:**
- Provide a LinkedIn URL (format: linkedin.com/in/username) for the best match quality
- Alternatively provide first name, last name, city, and state together
- Choose one or more outputs; use `required_outputs` to return only records that matched those outputs

Input Schema

TableJSON Schema

Name	Required	Description
`zip`	No	5 digit ZIP code
`city`	No	City name
`last`	No	A person's last name
`email`	No	A known email address for the contact
`first`	No	A person's first name
`state`	No	Two-character state code (US)
`li_url`	No	A LinkedIn URL (accepts https://www.linkedin.com/in/username, www.linkedin.com/in/username, or linkedin.com/in/username)
`address`	No	Street address
`country`	No	Two-character country code (currently only US data is supported)
`outputs`	Yes	Contact data to return: business_email, consumer_mobile, consumer_email, consumer_address
`required_outputs`	No	Subset of outputs a record must have to be returned
`cfg_max_emails_b2c`	No	Maximum consumer email addresses to return per record
`cfg_max_phones_b2c`	No	Maximum consumer phone numbers to return per record

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

A4.4/5.0

Behavior4/5

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

Annotations already declare this a read-only, idempotent, non-destructive operation, lowering the behavioral disclosure burden. The description adds useful context beyond that: it performs a single match, supports multiple input strategies, and explains that required_outputs filters results to matched records. This gives the agent a meaningful model of behavior beyond the structured annotations.

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, front-loaded with the core purpose, and rounds out with a focused set of actionable tips. Every line adds information, there is no filler, and the scannable bullet list makes invocation guidance easy to parse.

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 the 13-parameter schema, rich annotations, and an output schema, the description covers the essential invocation choices without needing to repeat field details. It mentions match quality, alternative input schemes, and output filtering behavior. It stops short of stating data limitations such as country restrictions beyond the schema, so it is strong but not exhaustive.

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 100%, so the schema documents each parameter's meaning already. The description adds genuinely useful parameter semantics by explaining the two valid input strategies (LinkedIn URL alone, or first/last/city/state together) and by clarifying the purpose of outputs vs. required_outputs. That is meaningful value beyond the per-parameter descriptions.

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 clearly identifies the tool's job with a specific verb and resource: enrich a business contact with their full B2C profile, and names the exact data types returned. The opening sentence distinguishes this from sibling enrichment tools by tying it specifically to 'B2B2C' / 'Business to Business to Consumer' requests.

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 explicitly states when to use the tool ('Use this tool when users ask for B2B2C...'), which gives the agent routing guidance. It does not explicitly name alternative tools or provide when-not conditions, but it does give clear invocation scenarios and best-match input strategies.

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.

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