Skip to main content
Glama

Mark all threads read

conversation_mark_all_read

Clear the unread badge across every thread on one LinkedIn account. Blunt and account-wide — it marks everything read, including threads nobody has looked at, so confirm before using it to tidy a badge. For one thread use conversation_update.

Input Schema

NameRequiredDescriptionDefault
linkedAccountIdYesLinkedIn account id, from `linkedin_account_list`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare the mutation safety profile, but the description adds meaningful behavioral context: the operation is account-wide, marks even unread threads nobody has looked at, and should be confirmed before use. It does not cover permissions, reversibility, or response behavior, but it substantially improves transparency beyond the annotations.

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 description is three tightly written sentences with no waste. Impact and caution are front-loaded, and the sibling alternative is mentioned at the end where it is most useful.

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

Completeness5/5

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

For a one-parameter mutation tool with annotations and no output schema, the description covers purpose, scope, side effect, caution, and the relevant sibling alternative. Nothing an agent needs in order to call it correctly is missing.

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?

There is one parameter (`linkedAccountId`) with 100% schema description coverage, including the source tool for obtaining the value. The description does not add syntax or meaning beyond the schema, so the baseline 3 is appropriate.

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?

States a specific verb and resource: clearing the unread badge across every thread on one LinkedIn account. It also explicitly distinguishes itself from the single-thread alternative `conversation_update`, so an agent can choose correctly without opening schemas.

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

Usage Guidelines5/5

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

Provides explicit when-to-use context and a clear alternative: use this for account-wide badge clearing, but use `conversation_update` for one thread. It also advises confirming before use because the effect is blunt and account-wide.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources