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Taplio for LinkedIn

List comment drafts

list_comment_drafts
Read-onlyIdempotent

List the user's LinkedIn comment drafts, newest first. A draft keeps the same record for its whole life, so this is also the queue and the history: status draft means still editable, pending means committed and waiting for its send slot, and posted, failed or cancelled are final. Use it to find a draft to edit or commit, or to review what earlier commits did.

Input Schema

NameRequiredDescriptionDefault
limitNoMax comment drafts to return. Defaults to 25, capped at 100.
cursorNoPagination token from a previous response; omit for the first page.
statusNoFilter by status: draft (written, not committed), pending (committed and queued to post), posted (live on LinkedIn), failed (posting was attempted and did not go through), cancelled (blocked before posting). Omit for all five.

Output Schema

NameRequiredDescriptionDefault
itemsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover the read-only, idempotent, closed-world safety profile. The description adds non-obvious context: that a draft keeps one record for its whole life, making this both the queue and the history, and which statuses are mutable vs. final. That lifecycle model is genuine value beyond the annotations and schema.

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?

Front-loaded with the verb and resource, then the lifecycle model, then usage. Three tight sentences with little waste, though the middle lifecycle sentence runs long and partly overlaps the schema's status descriptions.

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 an output schema handling return values and full schema coverage of the three params, the description only needs to add the conceptual model and usage — which it does. An agent has everything required to invoke and interpret results correctly.

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 coverage is 100%, and the status enum with its five meanings is already fully documented in the schema, so the description largely restates it. The only parameter-adjacent addition is the "newest first" ordering, so the baseline of 3 is 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 and resource ("List the user's LinkedIn comment drafts") plus the ordering ("newest first"). The "comment drafts" resource is naturally distinguishable from the general list_drafts sibling, though no sibling is named explicitly.

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?

"Use it to find a draft to edit or commit, or to review what earlier commits did" gives clear when-to-use contexts. It stops short of naming the alternative tools (get_comment_draft, list_drafts) or stating when not to use this one.

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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