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xmagnet

enrich_contact

Enrich a contact with verified work email, phone, LinkedIn, company details, and social profiles. Provide email or full name + company.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoContact's email address
companyNo
last_nameNo
contact_idNoXmagnet contact ID to enrich in-place
first_nameNo
linkedin_urlNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / contact_id / description
      Previous value: -"CRM contact ID to enrich in-place"New value: +"Xmagnet contact ID to enrich in-place"
  2. Added

TDQS

A3.6/5.0
Behavior2/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, so the agent knows it may mutate, but the description does not clarify whether enrichment writes to the CRM, consumes credits, or is reversible. The 'contact_id' schema hint about 'enrich in-place' is not carried into the main description, leaving side effects and data handling opaque.

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?

Two concise sentences front-load the outcome and immediately follow with input requirements. Every word earns its place; no verbosity or redundancy.

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

Completeness2/5

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

With no output schema and minimal annotations, the description should explain expected return value, whether it performs mutations, and any dependencies (e.g., credit costs). It does not. While the purpose and basic inputs are clear, an agent lacks critical behavioral context to safely and confidently invoke this tool.

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 low (only email and contact_id have descriptions), but the description adds key semantics by explaining the alternative input options: email OR full name + company. This clarifies the relationship between first_name, last_name, and company that the schema itself lacks. However, contact_id and linkedin_url roles remain under-explained.

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 action ('Enrich a contact') and specifies the exact data types returned ('verified work email, phone, LinkedIn, company details, and social profiles'). It distinguishes itself from siblings like find_email (single email lookup), validate_email, and company_intelligence by focusing on comprehensive enrichment.

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 instruction 'Provide email or full name + company' gives useful input guidance, but it does not explicitly state when to choose this tool over alternatives like find_email, get_contact_details, or update_contact. Usage context is implied but not formalized with when-to-use/when-not-to-use guidance.

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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