get_knowledge_gapsSurface unanswered questions from chatbot conversations, grouped by topic.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No | ||
| topic | No | ||
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / clientAdded value: +{
+ "description": "Agency only: target a managed client org by its id to query that client's data. Omit for your own.",
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and openWorldHint=false, so safety and world scope are covered. The description adds a behavioral trait: results are grouped by topic. However, it does not disclose details like aggregation windows, whether results are global or per-client, or any filtering effects beyond topic.
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 immediately states the tool's purpose. Every word adds value without redundancy.
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 is low complexity with no required parameters and a read-only hint, but without an output schema the description does not explain what the returned data looks like or how parameters like 'days' and 'limit' affect results. It is minimally sufficient for selection but not for fully correct invocation.
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 has only 25% description coverage (only 'client' is described). The description mentions 'grouped by topic', which gives partial meaning to the 'topic' parameter, but it does not explain 'days' or 'limit' semantics beyond their schema types and defaults. Since schema coverage is below 50%, the description should compensate but does not.
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's function with a specific verb ('surface'), resource ('unanswered questions from chatbot conversations'), and grouping behavior ('grouped by topic'). It uniquely distinguishes itself from sibling tools like get_conversations or get_analytics_summary by focusing on knowledge gaps.
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 usage when you want to discover unanswered questions from chatbot conversations, but it does not explicitly state when to use this tool instead of alternatives, nor does it mention any exclusions or prerequisites. There is no reference to sibling tools or use cases.
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.
Most tools are clearly distinct, but get_conversations and search_by_intent both return conversations, and get_hot_leads vs get_lead_enrichment have some overlap. Descriptions clarify their specific purposes, so ambiguity is minimal.
The majority of tools follow the get_* pattern, but search_by_intent deviates. Otherwise, naming is consistent and readable.
With 8 tools, the count is well-scoped for the server's purpose. Each tool focuses on a distinct aspect of buyer intent, from summaries to specific searches, without unnecessary bloat.
The server covers core needs: overview, listing, detail, enrichment, MEDDIC summary, knowledge gaps, and search. Missing are non-read operations and a general lead list (only hot leads), but these are minor gaps for an analytics-focused server.