get_conversation_detailGet full details of a single conversation: MEDDIC signals, engagement metrics, enrichment data, and any owner-configured discovery field values captured during the chat (e.g. company size, current tooling, hiring status).
| Name | Required | Description | Default |
|---|---|---|---|
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. | |
| conversation_id | Yes |
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 indicate a safe read-only operation (readOnlyHint=true). The description adds meaningful context about the scope of data returned, including owner-configured discovery fields, which goes beyond the simple 'get details' phrasing. No contradictions with 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 immediately states the tool's purpose and then elaborates with concrete examples. Every phrase adds value, with no redundancy or filler.
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?
For a single-resource read tool with good annotations and a clear parameter schema, the description sufficiently covers what data is returned. It does not explain error conditions or rate limits, but given the simplicity of the operation, these are not critical for basic 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?
Schema description coverage is only 50% (client is described, conversation_id is not). The description does not clarify what conversation_id should look like or how to obtain it, and does not compensate for the undocumented required parameter. The mention of 'single conversation' is the only hint.
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 it retrieves full details of a single conversation, listing specific content categories (MEDDIC signals, engagement metrics, enrichment data, discovery fields). This distinguishes it from sibling tools like get_conversations (list) and get_meddic_summary (summary only).
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 use when you need detailed data for one specific conversation, contrasting with listing tools. However, it provides no explicit when-not-to-use or alternative tool references, so usage guidance is only implied rather than stated.
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.