reply_to_commentReply to a comment on one of your published posts.
Supported platforms: X (reply tweets), Threads, Instagram, Facebook, YouTube. Not supported: LinkedIn, Pinterest, TikTok.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Your reply text | |
| threadId | Yes | The inbox thread ID to reply to |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a write operation (readOnlyHint=false, destructiveHint=false). The description adds platform coverage, which is useful, but it does not disclose potential side effects like immediate public posting, rate limits, or authentication requirements. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two short sentences: the first states the action clearly, and the second concisely lists platforms. Every word earns its place, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with strong annotations and full schema coverage, the description is mostly complete. The platform list adds valuable context, but it lacks any mention of what happens after the reply is sent (e.g., immediate posting, return value), which would be helpful given there is no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add parameter details, and there is a slight mismatch between the tool's 'comment' focus and the parameter description's 'inbox thread ID,' which could confuse agents about what threadId actually refers to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Reply to a comment on one of your published posts' with a specific verb and resource. The supported/unsupported platform list further clarifies scope and distinguishes this from conversation-level or drafting tools like reply_to_conversation and draft_reply.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit platform support and exclusions (X, Threads, Instagram, Facebook, YouTube vs. LinkedIn, Pinterest, TikTok), giving clear context on when to use the tool. However, it does not directly name alternative tools for unsupported cases, stopping short of full usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With 148 tools, there is significant overlap. For example, generate_content, publish_ai, generate_post_bundle, and request_project_content all generate content; get_analytics, get_unified_analytics, get_post_analytics, get_ad_performance, and get_unified_ad_report all fetch performance metrics; and list_inbox vs list_conversations blur comment and conversation management. Descriptions help, but boundaries between tools are often unclear.
Most tools follow a verb_noun pattern (e.g., list_teams, create_goal, delete_post), but there are notable deviations: create_library_item vs save_to_library, publish_content vs publish_ai, schedule_content vs schedule_content_advanced, and connect_platform vs connect_connector. Mixed prefixes like 'autopilot_', 'check_', and 'get_' are fine, but overlapping verbs and a hyphen in 'connect_linkedin-page' reduce consistency.
148 tools is extreme for any server. Even for a broad social media management platform, this is far beyond what an agent can effectively navigate. The count is unwieldy and suggests the surface should be split into multiple focused servers (publishing, analytics, connectors, workflows, etc.).
The core social publishing workflow is well covered (create, schedule, publish, edit, delete, retry), and there are extensive features for analytics, workflows, connectors, and AI agents. However, some resources have CRUD gaps: no update/delete for brand voices, no delete_project, no update/delete for Product Hunt goals, and no explicit get_workflow. These are workable but notable omissions.