get_meddic_summaryAggregate MEDDIC signal distribution across all conversations.
| Name | Required | Description | Default |
|---|---|---|---|
| days | 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?
Annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds minimal extra behavior by indicating it aggregates/summarizes rather than returns raw conversations, but does not disclose return format, performance considerations, or other side effects. This is adequate but not rich.
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, direct sentence with no filler. It states the core action and scope concisely, earning its place 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?
Given the tool is simple and read-only with two optional parameters, the description gives the core idea ('aggregate MEDDIC signal distribution') but is incomplete without an output schema or details on what form the distribution takes. It lacks information about the return value and any caveats, but the tool's simplicity and read-only annotation keep it at a minimum viable level.
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 50% (only the 'client' parameter has a description; 'days' has none). The tool description does not compensate by explaining either parameter, and the meaning of 'days' (e.g., a lookback window) is left ambiguous. Since coverage is not high and the description adds no param semantics, the score is low.
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 'Aggregate MEDDIC signal distribution across all conversations' uses a specific verb (aggregate), identifies the resource (MEDDIC signal distribution), and defines scope (across all conversations). This clearly distinguishes it from siblings like get_conversation_detail (individual conversations) and get_analytics_summary (generic analytics).
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. The description does not mention any exclusions, prerequisites, or contrasting tools. The usage is only implicit from the name and scope, which does not meet the bar for explicit context.
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.