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Glama

linkedin_voyager_request

Call LinkedIn's internal Voyager API using a saved browser session to retrieve profile, network, or job data not exposed in the standard UI. Treat failures as normal when routes change.

Instructions

Llamada directa a la API interna de LinkedIn (Voyager) reutilizando la sesión del navegador. Atajo avanzado para leer datos que la interfaz no expone cómodamente. No es una API pública: las rutas cambian sin aviso, así que trata los fallos como normales y usa las otras herramientas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNo
pathYesRuta relativa, por ejemplo "me" o "identity/dash/profiles?q=..."
methodNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full load and does disclose meaningful behavior: it reuses the browser session, is not a public API, routes change without warning, and failures should be treated as normal. It omits auth/session prerequisites and the fact that the method enum permits destructive writes (DELETE/PUT), but the fragility and error-tolerance disclosure is genuinely valuable.

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?

Three tight sentences that front-load the core purpose before the caveats. No filler, though the caveat sentences could be marginally compressed.

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

Completeness3/5

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

No annotations and no output schema, so the description must do more, and it does warn about volatility and error handling. However, it never covers the write capability implied by POST/PUT/DELETE (it frames the tool as 'para leer datos'), return format, or parameter meaning, leaving gaps for a 3-parameter request tool.

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?

Schema coverage is only 33% – only 'path' is described – and the description adds nothing about parameter semantics. It never explains the path format, the meaning of 'method' variants, or the shape of 'body', leaving 'body' and 'method' essentially undocumented.

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 states a specific verb and resource ('Llamada directa a la API interna de LinkedIn (Voyager) reutilizando la sesión del navegador'), which is unambiguous. It positions itself as the low-level escape hatch versus the many higher-level siblings by calling itself an 'atajo avanzado' for data the UI doesn't expose comfortably.

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?

It gives a clear condition for use (reading data the interface doesn't expose comfortably) and a fallback ('usa las otras herramientas') when the call fails. It stops short of naming specific alternatives or exclusions beyond 'the other tools', so it falls just below fully explicit guidance.

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