Analyze a recorded or transcribed call to return summary, sentiment, topics, objections, and follow-up suggestions for conversation review and next steps.
AI analysis of a recorded/transcribed call. Returns summary, sentiment, topics, objections, and follow-up suggestions for the conversation.
| Name | Required | Description | Default |
|---|---|---|---|
| callId | Yes | Call UUID to analyze (must have a transcript) |
Changes observed during successful MCP inspections.
v4.71.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden and does not meet it. It does not say whether this triggers a fresh AI run or reads cached results, whether it is costly or slow, whether it is idempotent, or what happens when the transcript is missing.
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?
Two sentences, front-loaded with purpose and then the return contents, with zero filler. Nothing in it is redundant or misplaced.
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 enumeration of returned fields is genuinely necessary and well done. However, gaps remain around latency, cost, freshness/caching, and failure behavior for a call without a transcript, which matter for an AI-processing tool.
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% and the single callId parameter is already documented with the transcript prerequisite, so the description cannot add parameter meaning. Baseline 3 applies for a high-coverage schema.
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?
States a specific verb (AI analysis) and resource (recorded/transcribed call), and enumerates the outputs produced: summary, sentiment, topics, objections, follow-ups. It does not distinguish itself from near-siblings such as conduyt_get_post_call_insight or conduyt_ai_call_disposition, which an agent could easily confuse with this tool.
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 never says when to choose this over conduyt_get_call, conduyt_get_post_call_insight or conduyt_ai_call_disposition. The only precondition (a transcript must exist) lives in the schema, not the description, so there is essentially no routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.