summarize_mentionsCluster and summarize recent social mentions by theme and sentiment using AI.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max mentions to analyze (default: 30, max: 50) | |
| since | No | ISO date string — only include mentions after this date | |
| platform | No | Filter to a specific platform | |
| keyword_id | No | Filter to mentions matching a specific keyword |
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 signal non-read-only (readOnlyHint=false), potential side effects (openWorldHint=true), non-idempotency, and non-destructiveness. The description adds 'using AI' as a behavioral trait but does not disclose costs, non-determinism, or what the tool does with empty/edge-case inputs. It does not contradict 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that packs the core purpose into a compact statement. Every word contributes to the meaning, with no filler or redundant phrases.
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?
There is no output schema, so the description should hint at the return format; it mentions 'theme and sentiment' but not whether the output is a text narrative or structured clusters. The tool has 4 optional parameters and no required fields, which is simple, but the absence of output details leaves the agent guessing about the result structure.
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 expand on parameter semantics beyond the schema, but the schema is self-sufficient with clear parameter descriptions. No extra value is added, but none is needed.
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 the action ('Cluster and summarize'), the resource ('recent social mentions'), and the method ('by theme and sentiment using AI'). This distinguishes it from sibling tools like get_analytics or get_inbox_summary by specifying a unique output style and domain.
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 implies when to use this tool (when a thematic and sentiment-based summary of social mentions is needed) but does not explicitly contrast it with alternatives or mention exclusions. It lacks direct guidance on when not to use it, which is a minor gap given the large sibling list.
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.