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List Tool Execution Logs

list_tool_execution_logs
Read-onlyIdempotent

List per-call voice-agent tool execution traces — outcome, latency, timeout, and a redacted result preview for each routed tool call. Mirrors GET /api/v1/tool-execution-logs. Answers "did this tool call actually work".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
fromNo
limitNo
call_idNoCall/conversation id filter.
tool_nameNoTool name filter (e.g. send_sms).
business_idNoBusiness ID (optional only when the token can access exactly one business).
tool_call_idNoExact tool_call_id filter.
outcome_statusNoOutcome envelope status filter (e.g. success, needs_clarification, unavailable, queued, retryable_delay, hard_failure).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
logsNo
errorNoPresent when success is false
successYesWhether the tool completed successfully
business_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / call_id / description
      Previous value: -"Vapi call/conversation id filter."New value: +"Call/conversation id filter."
  2. Added

TDQS

A4.3/5.0
Behavior4/5

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

Beyond the read-only/idempotent annotations, the description discloses that result previews are redacted, that latency and timeout are captured, and that logs are per-call. This adds meaningful behavioral context without contradicting the annotations, though it does not mention ordering, pagination, or data availability delays.

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

Conciseness5/5

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

Two sentences with no filler: the first fronts the resource and key data fields, the second gives the API mapping and the practical question the tool answers. Every clause earns its place.

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?

For an optional-filter list operation with a read-only annotation and an output schema, the description provides everything needed to select and invoke the tool: resource scope, output contents, API correspondence, and the intended diagnostic use. No critical information is missing.

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

Parameters3/5

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

The schema already documents five of eight parameters with descriptions, while the remaining three (to, from, limit) have self-evident names, formats, and defaults. The description adds little direct parameter guidance, but it does contextualize the outcome_status filter through 'outcome'. This is adequate but not exemplary given the 63% schema coverage.

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?

Description uses a specific verb and resource ('List per-call voice-agent tool execution traces') and enumerates meaningful output aspects: outcome, latency, timeout, and redacted result preview. It also connects the tool to an explicit API endpoint and a user-facing question ('did this tool call actually work'), which clearly separates it from generic list tools.

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

Usage Guidelines4/5

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

The description provides a clear use context by framing the tool as the way to answer whether a tool call actually worked and by mentioning per-call routed tool executions. It does not explicitly name alternatives or exclusion conditions, but the context is strong enough for an agent to select this tool for log/trace inspection.

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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