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register_recording_target

Register where BYOS call recordings go, so you can pass "record": true to run_test and have that call’s audio teed to YOUR OWN storage. wordis-bond keeps only a pointer (the run’s recording_url), never the audio. "callbackUrl" is a public https endpoint that returns a presigned PUT URL per recording (so wordis-bond never holds your cloud credentials). Pro/enterprise capability, bundled free — you pay your own storage; starter → 402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesA label for this target.
trackNoWhich side to capture (default inbound = the agent).
callbackUrlYesPublic https endpoint that mints a presigned PUT URL per recording.

TDQS

A4.6/5.0
Behavior5/5

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

Despite having no annotations, the description goes into significant behavioral detail: it states wordis-bond keeps only a pointer and never audio, explains the presigned PUT URL mechanism to avoid holding cloud credentials, and discloses plan limitations (starter gets 402). This exceeds what an annotation would typically provide.

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?

The description is three sentences, each dense with information: purpose, behavior, callback mechanism, pricing. No wasted words, and the key purpose is front-loaded.

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

Completeness4/5

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

The description covers the tool's purpose, workflow, constraints (public https, presigned PUT), and plan restrictions. It doesn't describe the response format, but for a simple registration tool with no output schema, this is a minor gap. The overall context is complete enough for an agent to use it correctly.

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?

The schema already covers all parameters with descriptions (100% coverage), so the baseline is 3. The description adds meaningful context beyond the schema: it explains that callbackUrl must be a public endpoint that mints presigned URLs per recording and clarifies that 'inbound' means the agent. This adds value without being redundant.

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's purpose: 'Register where BYOS call recordings go' and explains its usage in the context of run_test with record:true. This is a specific verb+resource pair that distinguishes it from sibling tools like run_test or ingest_call.

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 clear context on when to use the tool (when you want call recordings teed to your own storage) and ties it to run_test's record:true flag. It does not explicitly mention alternatives or exclusions, but the usage context is unambiguous.

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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TDQS

A4/5.0
Disambiguation4/5

Most tools are clearly distinct by resource (monitors, suites, flows, numbers, recordings), but run_test and test_flow could be confused since both execute tests, though their scopes differ. The descriptions help disambiguate them.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (create_, get_, list_, run_, verify_, etc.), with no camelCase or mixed conventions. Even compound names like get_monitor_health and verify_number_confirm remain predictable.

Tool Count4/5

At 16 tools, the set is slightly above the optimal 3-15 range, but the breadth of the voice-agent testing/monitoring domain justifies each tool's existence. It feels well-scoped rather than bloated.

Completeness2/5

The tool set lacks update/delete operations for most entities (monitors, suites, flows) and omits a get_run tool to retrieve individual live test results, leaving significant gaps that agents cannot work around. This will cause failures in lifecycle management and live-run result retrieval.

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