critique_postScore and critique post content with AI-powered suggestions. Returns a quality score (0-100), breakdown, and improvement tips.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Post text to critique (required) | |
| mediaUrl | No | Optional media URL for media score | |
| platforms | Yes | Target platforms for scoring context (required) |
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?
The description mentions 'AI-powered' and the return object, but the annotations indicate readOnlyHint=false, implying possible side effects. The description does not disclose whether the critique is stored, triggers other actions, or calls external APIs, creating ambiguity about the tool's state-modifying behavior.
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?
Two focused sentences convey the purpose, method, and return value with no redundancy. The information is front-loaded and every word contributes meaning.
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?
With no output schema, the description covers the primary return values (score, breakdown, tips) but leaves gaps: it does not explain how platform context or mediaUrl influence the critique, nor does it mention potential side effects or errors. It is adequate but not comprehensive for an agent deciding to invoke it.
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?
The input schema covers all three parameters with descriptions, so the baseline is 3. The description adds context by mentioning the return score range and breakdown, but it does not explain how 'platforms' or 'mediaUrl' affect the scoring beyond what the schema already states.
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 uses a specific verb ('Score and critique') with a clear resource ('post content'), and it mentions the unique value of 'AI-powered suggestions'. It distinguishes itself from siblings like score_content by focusing on critique and improvement, though it does not name them explicitly.
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?
No guidance is provided on when to use this tool versus alternatives such as validate_content, approve_post, or edit_post. It does not state whether it is for pre-publish feedback or how it differs from approval/rejection workflows, leaving the agent without contextual decision support.
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.