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BACH-AI-Tools

Fresh Linkedin Profile Data MCP Server

Enrich Lead

enrich_lead

Fetch LinkedIn profile data to enrich leads with roles, skills, certifications, and more. Choose optional sections to build complete lead records for targeted outreach.

Instructions

1 credit per call. Using each extra option will cost another 0.5 credits. Maximum: 3 credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linkedin_urlYesExample value: https://www.linkedin.com/in/cjfollini/
include_honorsNoExample value:
include_skillsNoExample value:
include_coursesNoExample value:
include_patentsNoExample value:
include_projectsNoExample value:
include_volunteersNoExample value:
include_publicationsNoExample value:
include_organizationsNoExample value:
include_certificationsNoExample value:
include_profile_statusNoExample value:
include_company_public_urlNoExample value:

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv2.0.0

TDQS

D1.8/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it only reveals credit cost. It does not disclose what the call returns, whether it mutates anything, what side effects occur, or what 'extra option' means. The pricing information is useful but far from sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The text is very concise and the pricing cap is clearly stated. However, brevity comes at the expense of essential functional information; the sentence structure itself is efficient, but the content is incomplete for a tool with 12 parameters.

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

Completeness1/5

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

For a 12-parameter tool with no output schema, no annotations, and no functional description, this definition is severely inadequate. An agent has no idea what the tool enriches, what each option does, or what response to expect, making correct invocation largely guesswork.

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

Parameters2/5

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

Although schema description coverage is formally 100%, the descriptions are only 'Example value:' placeholders, with empty examples for 11 optional include_* parameters. The description adds no meaning about what these parameters control or what values they accept, leaving the agent without enough information to use them correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description only covers credit pricing ('1 credit per call...') and never states what the tool does, what data is enriched, or how the LinkedIn URL is used. This is effectively a missing purpose statement; the agent must infer function from the tool name alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool over its many siblings, such as get_extra_profile_data or get_open_profile_status. There is no mention of prerequisites, alternatives, or conditions, so an agent cannot decide when this tool is appropriate.

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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