Versium REACH

B2B Listgen Estimate Mcp Tool

b2b_listgen_estimate

Read-onlyIdempotent

Estimate business contact audience sizes with firmographic and role-based targeting using Versium's B2B Persona Audience Builder (B2B Listgen) API.

**Tips for Best Results:**
- Start with a single output type and add filters incrementally
- Combine geography with company size or industry filters for precise targeting
- Use title_seniority for leadership focus, or department for functional targeting
- Provide SIC codes when you need strict industry alignment
- To check values for fields with large, high-cardinality option sets, call the B2B Persona Listgen Options tool
- Toggle `cfg_b2cloc` to switch between business-location and home-location targeting

Input Schema

TableJSON Schema

Name	Required	Description
`d_sic`	No	Legacy 4-digit SIC codes
`d_zip`	No	U.S. ZIP codes (5-digit codes)
`output`	No	Campaign type specification. Must include exactly one: "email" or "oa". Email should almost always be used; OA can ONLY be used for audience targeting in ad platforms and will return hashed data
`d_naics`	No	Raw 2- to 6-digit NAICS codes. Use the options tool to retrieve valid values
`d_state`	No	U.S. states (2-letter codes)
`rd_role`	No	Job roles. Use the options tool to retrieve valid values
`d_county`	No	U.S. counties (full county names)
`cfg_b2cloc`	No	Whether location filters apply to business location (0) or employee home location (1)
`max_records`	No	Cap the number of records returned by the eventual list build
`d_city_state`	No	U.S. City and state combinations, e.g. "Austin, TX"
`d_salesvolume`	No	Company revenue range, in the format "min-max" (e.g. "100000-1000000" for $100K to $1M)
`rd_department`	No	Job departments. Use the options tool to retrieve valid values
`d_numemployees`	No	Company size range, in the format "min-max" (e.g. "100-50000")
`rd_title_seniority`	No	Title seniority levels

Output Schema

TableJSON Schema

Name	Required	Description	Default
`results`	No
`query_id`	No
`warnings`	No
`input_query`	No
`num_matches`	No
`num_results`	No
`match_counts`	No

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": {
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "num_matches": {
+      "type": [
+        "integer",
+        "null"
+      ]
+    },
+    "num_results": {
+      "type": [
+        "integer",
+        "null"
+      ]
+    },
+    "query_id": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "results": {
+      "properties": {
+        "estimated_credits": {
+          "type": [
+            "string",
+            "integer",
+            "number",
+            "boolean",
+            "object",
+            "array",
+            "null"
+          ]
+        },
+        "estimated_match_credits": {
+          "type": [
+            "string",
+            "integer",
+            "number",
+            "boolean",
+            "object",
+            "array",
+            "null"
+          ]
+        },
+        "estimated_record_count": {
+          "type": [
+            "string",
+            "integer",
+            "number",
+            "boolean",
+            "object",
+            "array",
+            "null"
+          ]
+        },
+        "estimated_records_available": {
+          "type": [
+            "string",
+            "integer",
+            "number",
+            "boolean",
+            "object",
+            "array",
+            "null"
+          ]
+        },
+        "estimated_time": {
+          "type": [
+            "string",
+            "integer",
+            "number",
+            "boolean",
+            "object",
+            "array",
+            "null"
+          ]
+        },
+        "estimated_time_in_seconds": {
+          "type": [
+            "string",
+            "integer",
+            "number",
+            "boolean",
+            "object",
+            "array",
+            "null"
+          ]
+        },
+        "preview": {
+          "items": {
+            "items": {
+              "type": [
+                "string",
+                "integer",
+                "number",
+                "boolean",
+                "object",
+                "array",
+                "null"
+              ]
+            },
+            "type": "array"
+          },
+          "type": [
+            "array",
+            "null"
+          ]
+        }
+      },
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "warnings": {
+      "items": {
+        "type": [
+          "string",
+          "integer",
+          "number",
+          "boolean",
+          "object",
+          "array",
+          "null"
+        ]
+      },
+      "type": [
+        "array",
+        "null"
+      ]
+    }
+  },
+  "type": "object"
+}

First observedAug 19, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds valuable context by framing the operation as an estimate of audience sizes rather than a list build. It also discloses the cfg_b2cloc behavior for switching between business-location and home-location targeting, which is useful beyond the schema.

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 front-loaded with a clear purpose sentence and then organized into concise, scannable bullet tips. Each tip earns its place by providing actionable guidance for a complex 14-parameter tool, with no redundant filler.

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 the tool's complexity, the description is complete: it states the core purpose, offers filter selection strategies, points to the options tool for valid values, and clarifies location targeting behavior. The output schema and annotations cover return values and safety, so the description does not need to repeat those.

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 the baseline is 3, but the description adds meaningful parameter guidance: it explains when to use title_seniority vs department, when to provide SIC codes, and how to interpret cfg_b2cloc. These tips go beyond the schema definitions by linking parameters to targeting strategies.

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 opens with a specific verb and resource: 'Estimate business contact audience sizes with firmographic and role-based targeting using Versium's B2B Persona Audience Builder (B2B Listgen) API.' This clearly distinguishes the tool from siblings like b2c_listgen_estimate (B2C) and b2b_listgen_options (options lookup).

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 'Tips for Best Results' section provides concrete guidance on when to use specific filters, such as using title_seniority for leadership focus, department for functional targeting, and SIC codes for strict industry alignment. It also explicitly directs users to call the B2B Persona Listgen Options tool for high-cardinality field values, though it does not explicitly contrast with the B2C sibling tool.

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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