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easyonthesauce

elevenlabs-agents-mcp-server

Get Call Outcome Report

elevenlabs_get_call_report
Read-onlyIdempotent

Retrieve a compact call outcome report including status, success evaluation, summary, duration, termination reason, data collection, and evaluation criteria results.

Instructions

Get a compact outcome report for a call/conversation: status, success evaluation, summary, duration, termination reason, extracted data-collection results, and evaluation criteria results. Does NOT include the transcript (use elevenlabs_get_call_transcript).

Note: analysis fields (call_successful, summary, data collection) are only populated after the call ends and post-call processing completes (status 'done'). If status is 'initiated'/'in-progress'/'processing', poll again or use elevenlabs_wait_for_call_completion.

Args:

  • conversation_id (string)

Returns JSON: { conversation_id, status, call_successful, summary, duration_secs, termination_reason, error, cost_credits, evaluation_criteria_results, data_collection_results, transcript_turns, has_audio }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversation_idYesConversation ID returned by elevenlabs_make_outbound_call
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint, idempotentHint, and non-destructive behavior. The description adds crucial context that analysis fields are only populated after call completion and post-processing (status 'done'), which aligns with and enhances the annotations. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose statement, a note about limitations, and separate Args/Returns sections. While slightly lengthy, every sentence adds value and nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one parameter and no output schema, the description fully covers the return fields and polling behavior. It provides enough detail for an agent to understand when to use the tool and how to interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description explicitly lists the parameter 'conversation_id (string)' in the Args section, adding context that it is the ID returned by elevenlabs_make_outbound_call. This adds meaning beyond the schema's description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

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 outcome report including status, success evaluation, summary, and other specific fields. It distinguishes itself from the sibling tool elevenlabs_get_call_transcript by explicitly stating it does not include the transcript.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit guidance on when to use this tool (to get call outcome) and when not to (use elevenlabs_get_call_transcript for transcript). It also advises on polling behavior if the call is not yet complete, directing to use elevenlabs_wait_for_call_completion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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