Teamtailor Ops Control Plane

by app.vercel.ats-ops-control-plane-gateway

get_interview

Get one interview. Interview records for process load and feedback obligation analysis.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	interview ID.
`include`	No
`query_json`	No	Additional documented query parameters, including bracketed filter keys.
`path_params`	No	Values for path placeholders such as {id} in a write operation.
`detail_profile`	No	operational (default) returns a compact recruiting-ops projection with names, prose, and other personal fields omitted; pass full to include the raw API payload with those fields.	operational

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed4 schema fields changedJun 7, 2026

changedInput schema / properties / detail_profile / default
```
Previous value: -"full"New value: +"operational"
```

changedInput schema / properties / detail_profile / description

Previous value: -"full returns raw API payload; operational returns a compact recruiting-ops projection."New value: +"operational (default) returns a compact recruiting-ops projection with names, prose, and other personal fields omitted; pass full to include the raw API payload with those fields."

addedInput schema / properties / path_params

Added value: +{
+  "additionalProperties": {
+    "type": "string"
+  },
+  "description": "Values for path placeholders such as {id} in a write operation.",
+  "type": "object"
+}

removedInput schema / properties / reason

Removed value: -{
-  "description": "Required when detail_profile=full.",
-  "maxLength": 500,
-  "minLength": 12,
-  "type": "string"
-}

First observedJun 5, 2026

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as return format, permissions, or whether the operation has side effects. The name 'get' implies read-only, but the description fails to confirm this or mention any limitations.

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 concise, with only two sentences that directly state the tool's purpose and context. There is no redundant or irrelevant information.

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

Completeness2/5

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

The tool has complex parameters and no output schema, yet the description is too thin. It does not explain what 'process load and feedback obligation analysis' means, nor does it clarify return values or usage prerequisites. The path_params schema description even mentions 'write operation', which is inconsistent with a getter tool.

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?

Schema description coverage is 80%, which is high, so the baseline is 3. The description adds no parameter-level detail beyond the schema, but the schema itself provides good documentation, especially for detail_profile with its enum and default.

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 'Get one interview' with a specific verb and resource, and the phrase 'Interview records for process load and feedback obligation analysis' adds context. It distinguishes from sibling list_interviews by emphasizing the singular nature.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like list_interviews. The implied use case of 'process load and feedback obligation analysis' is mentioned, but there are no exclusions or alternative references.

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

C2.8/5.0

Disambiguation4/5

Most tools have clearly distinct purposes due to specific resource names and detailed descriptions. However, the large number of similar get/list pairs (e.g., get_activity, get_answer) could cause an agent to misselect without careful reading, slightly reducing clarity.

Naming Consistency5/5

The naming follows a strict and predictable verb_noun pattern (list_/get_/create_/update_) using snake_case throughout. The only outlier is 'api_request', but it is a generic tool and does not break the overall pattern.

Tool Count3/5

With 62 tools, the set is large but arguably justified by the breadth of the Teamtailor domain (candidates, jobs, applications, interviews, notes, etc.). Still, it exceeds the typical 3-15 range for high coherence, making navigation heavy for agents.

Completeness3/5

The tool set provides comprehensive read access (get/list) for most entities, but lacks delete operations entirely and has limited update coverage (only candidate, job, job_application). The 'api_request' tool can compensate, but the surface has notable gaps in mutation capabilities.

Resources

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