b2b-lead-quant-mcp

score_email_deliverability_risk

Score B2B email lists 0-100 for bounce and spam-trap risk, then classify each address as send, review, or suppress before outbound campaigns.

Instructions

Score B2B email lists (0-100) for bounce & spam-trap risk and classify each address into send / review / suppress before outbound campaigns.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "emails": "<emails>"
    },
    "name": "score_email_deliverability_risk"
  }
}

Input Schema

Name	Required	Description	Default
`emails`	Yes	List of email addresses to score.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 20, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden, and it does disclose the scoring range and the three-way output classification. It says nothing about determinism, rate limits, cost, or whether the classification is per-address or aggregate, leaving meaningful behavioral gaps for a mutation-free but non-trivial analysis tool.

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?

One sentence that front-loads the core action (score B2B email lists), then the output semantics (0-100, risk type) and the action mapping (send/review/suppress). No filler.

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?

With no output schema, the description usefully explains what comes back: a 0-100 risk score and a per-address send/review/suppress label. That is adequate for a one-parameter tool, though it omits how the score is delivered (structure, aggregation) and any limits on list size.

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 coverage is 100% for the single 'emails' array parameter, whose description already states it is the list of addresses to score. The tool description adds no format, size-limit, or batching guidance beyond the schema, so the baseline 3 applies.

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?

States a specific verb (score) and resource (B2B email lists) plus the scale (0-100) and the resulting classification (send/review/suppress). This clearly separates it from sibling tools like find_and_verify_corporate_emails (find) and audit_domain_email_security (audit), though it never names an alternative explicitly.

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?

The phrase 'before outbound campaigns' implies the usage context, so an agent can infer when the tool is relevant. However, there is no statement of when NOT to use it, no prerequisites, and no routing to a sibling tool if the agent needs discovery/verification instead of scoring.

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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