Versium REACH

B2 B2 C Append Mcp Tool

b2b2c_append

Read-onlyIdempotent

Enrich a business contact in a marketing list with their consumer-side profile, so B2B audiences can be reached through consumer channels: with a LinkedIn URL or a name + city/state, append 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.

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 25, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral context beyond annotations: it explains the enrichment process, the role of required_outputs in filtering results, and the configurable maximum outputs (cfg_max_emails_b2c, cfg_max_phones_b2c). It does not contradict annotations and adds useful operational details, though it doesn't cover every edge case (e.g., error handling or rate limits).

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 well-structured with a clear purpose statement, usage guidance, and a bulleted 'Tips for Best Results' section. It is slightly longer than necessary but every sentence adds value, and the tips are practical. The front-loading of the core purpose and usage condition is effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists (per context signals), the description does not need to explain return values. It covers input options, output selection, configuration parameters, and the matching strategy. The description is complete enough for an agent to know how to invoke the tool correctly, including the US-only limitation (noted in the schema) and the required outputs behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and every parameter has a description. The description adds significant semantic value by explaining the two primary input modes (LinkedIn URL vs. name+city/state) and the purpose of required_outputs, which is not fully apparent from the schema alone. It also clarifies the relationship between outputs and required_outputs, making parameter usage unambiguous.

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 states the tool's purpose: enriching a business contact with consumer-side profile data, specifying the resource (business contact) and the action (append consumer data). It also names the target use case (B2B2C) and the data types involved, making it distinct from the sibling tools like c2b_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 explicitly states when to use the tool ('when users ask for B2B2C...') and provides best-practice tips for input formats (LinkedIn URL vs. name+city/state). However, it does not mention when not to use it or name alternative tools, which would earn a 5. The usage context is clear but lacks explicit exclusion criteria.

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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