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linkedin_profile_read

Fetches full profile data—headline, about, experience, education, skills, certifications, languages—for a LinkedIn member by URL, public ID, or 'me'.

Instructions

Lee un perfil completo (el propio por defecto): titular, acerca de, experiencia, educación, aptitudes, certificaciones e idiomas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNo"me" para el propio, o una URL / identificador público como "juan-perez-123".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
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 behavioral burden. It discloses that this is a read operation and lists the exact fields returned, which is meaningful. It does not mention session/auth prerequisites, rate limits, or whether a public identifier requires a logged-in session, leaving real gaps.

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?

A single, front-loaded sentence that leads with the action and resource before enumerating the returned sections. Efficient with no filler, though the field enumeration makes it slightly dense.

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 only one optional parameter, no annotations, and no output schema, the description compensates well by enumerating the returned sections, effectively serving as return-value documentation. It lacks only negative guidance (when not to use it) to be fully complete.

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 description coverage is 100%, so the single 'target' parameter and its 'me'/URL formats are already documented in the schema. The description adds the useful default ('el propio por defecto'), but otherwise does not extend parameter meaning beyond the schema, making the baseline 3 appropriate.

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 ('Lee') and resource ('perfil completo') and enumerates the sections returned (titular, acerca de, experiencia, educación, etc.), so an agent can tell it is a full-profile reader rather than a single-section reader. It does not explicitly name the sibling it differs from (e.g., linkedin_profile_sections), so it falls short of a 5.

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 parenthetical 'el propio por defecto' tells the agent the default target is the authenticated user's own profile, which is useful invocation context. However, there is no explicit when-to-use-vs-alternatives guidance, e.g., when to call this versus linkedin_profile_sections or the narrower linkedin_profile_headline/about tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.