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Glama

check_session

Check if your LinkedIn session is still active to avoid authentication failures when managing your account.

Instructions

Return whether the persisted LinkedIn session is still valid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
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. It clearly identifies a non-mutating read-style behavior (returning validity), but it does not explicitly state that no side effects occur or whether any authentication context is required to call it. Adequate for a zero-parameter check, but not rich.

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?

The entire description is one efficient sentence that front-loads the action ('Return') and the object ('persisted LinkedIn session'). Every word earns its place.

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?

For a zero-parameter validation tool, the description is largely complete: it communicates the core purpose and implies a boolean outcome. The only minor gap is that the return format is not explicitly stated and there is no usage context, but nothing is needed to invoke the tool correctly.

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?

The tool has zero parameters and the schema is empty, so parameter semantics are a non-issue. The baseline of 4 applies because the description need not compensate for any undocumented parameters.

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 ('Return') and resource ('whether the persisted LinkedIn session is still valid'). It is clearly distinguishable from siblings like login, which creates a session, and from content operations like create_post or update_headline.

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?

The description gives no explicit when-to-use guidance, such as 'call before authenticated operations to verify login state' or 'use login if the session is invalid.' The need to check a session is implied by the sibling toolset, but the description itself does not state it.

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