get_analyticsReturn a compact analytics summary for this workspace over the last days days (default 30): conversation counts + resolution rate, message volume + AI share, plus a live snapshot of today (UTC).
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look-back window in days for the conversation/message stats (default 30). |
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?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It explains what the tool returns (compact summary, specific metrics, live snapshot in UTC) but does not mention whether it is read-only, safe, or if there are any side effects. It is not misleading but lacks complete transparency about the operation's nature.
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 sentence that efficiently conveys the tool's output components and the parameter default. It is well-structured, front-loaded with the main purpose, and contains no superfluous words.
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?
The tool has one parameter, no output schema, and moderate complexity. The description explains the return value in reasonable detail (conversation counts, resolution rate, message volume, AI share, live snapshot in UTC). It lacks mention of error handling or response format, but for a simple analytics tool these are less critical. Overall, it provides sufficient context for correct usage.
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 schema already fully describes the single parameter 'days' (type, default, min/max, description). The description adds minimal extra meaning by reiterating the default of 30 days and placing it in the context of the analytics summary, but it does not provide new semantic information. With 100% schema coverage, a baseline of 3 is appropriate.
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 tool returns a 'compact analytics summary' for the workspace, specifying the metrics (conversation counts, resolution rate, message volume, AI share, live snapshot). The verb 'Return' and resource 'analytics summary' are specific, and the tool is easily distinguished from siblings like get_conversation or list_conversations.
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 no guidance on when to use this tool versus alternative tools (e.g., get_conversation, list_conversations). It does not mention when not to use it or suggest any prerequisites. The usage context is only implied by the tool's purpose.
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.
Tools are largely distinct, with clear purposes for knowledge management, conversations, FAQs, and setup. The only potential overlap is between 'search' (general help) and 'search_knowledge' (workspace KB), but descriptions clarify the context.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., add_knowledge, list_conversations, manage_faq). No mixing of conventions or vague verbs.
17 tools is well-scoped for a live-chat and AI agent workspace server. The set covers setup, knowledge base, conversations, FAQs, analytics, keywords, and embedding without being overwhelming.
The tool surface is comprehensive for core workspace management and support: setup, knowledge ingestion/search, conversation handling, FAQs, analytics, and keywords. Minor gaps like user management or advanced channel configuration, but nothing that critically hinders agent workflows.