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by io.github.Shree-git

Suggest Queue Slots

suggest_queue_slots
Read-onlyIdempotent

Suggest reusable queue slots derived from best-time insights for one or more platforms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idNoTeam ID to operate in team scope. Get available teams with list_teams. If omitted, uses personal scope.
timezoneNoOptional IANA timezone for the suggested local queue times.
platformsNoOptional platform list. If omitted, suggestions may be generated for all supported platforms.
slotsPerPlatformNoHow many suggested slots to return per platform.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the behavioral detail that slots are 'derived from best-time insights', which is useful. However, it does not disclose fallback behavior, preconditions, or the format of returned slots.

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 a single, concise sentence that front-loads the action ('Suggest reusable queue slots') and adds a key qualifier. No redundant wording or restating of the title.

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

Completeness2/5

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

There is no output schema, so the description carries the burden of explaining what the tool returns. It only says 'suggest reusable queue slots', leaving unclear what a slot looks like, how many are returned, or how they relate to scheduling workflows. Given the tool's complexity and 4 parameters, this is insufficient.

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 description coverage is 100%, with all four parameters documented in the input schema. The description adds no additional parameter-level meaning beyond saying 'one or more platforms', which is already expressed in the platforms parameter description. 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?

The description uses a specific verb ('Suggest') and a clear resource ('reusable queue slots'), further qualified by 'derived from best-time insights' and 'one or more platforms'. This distinguishes it from sibling tools like suggest_next_schedule_time (single suggestion) and get_best_times (insights).

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 guidance on when to use this tool versus alternatives. It implies use for multiple platforms via 'one or more platforms', but does not mention the closely related sibling suggest_next_schedule_time or any exclusion criteria.

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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TDQS

B3/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.).

Completeness3/5

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.

Resources