Zentrik

studies_generate_findings

Idempotent

Use this when the user wants draft findings from a Study that has responses. Generates evidence-grounded findings, or does nothing when current findings already cover the latest evidence. The result contains no participant identity or raw responses and still requires human review in Zentrik.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "studyRef": "<studyRef>"
    },
    "name": "studies_generate_findings"
  }
}

Input Schema

Name	Required	Description	Default
`studyRef`	Yes

Output Schema

Name	Required	Description
`item`	Yes
`changed`	Yes
`operation`	Yes
`expectedUpdatedAt`	No	Concurrency token for the next studies.update_draft call on this Study.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 25, 2026
- changedOutput schema / properties / item / additionalProperties
```
Previous value: -trueNew value: +{}
```

Changed3 schema fields changedOct 23, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

removedInput schema / properties / llm_model

Removed value: -{
-  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
-  "type": "string"
-}

changedInput schema / required

Previous value: -[
-  "studyRef",
-  "llm_model"
-]New value: +[
+  "studyRef"
+]

Changed3 schema fields changedSep 27, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "studyRef"
-]New value: +[
+  "studyRef",
+  "llm_model"
+]

First observedSep 21, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=true, openWorldHint=false), and the description adds real value on top: result contents are de-identified and exclude raw responses, output requires human review in Zentrik, and the call is a no-op when evidence is already covered. It does not say whether existing draft findings would be overwritten or what permissions are needed, so a small gap remains.

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?

Three sentences, front-loaded with the invocation trigger, then the effect, then the result constraints. There is no filler, though the opening 'Use this when the user wants…' phrasing is slightly wordier than needed.

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?

An output schema exists, so return shape need not be explained, and the annotations carry the safety profile. The description covers the trigger, the effect, the no-op case, and the privacy/review constraints — everything an agent needs aside from parameter format.

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

Parameters2/5

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

Schema description coverage is 0% and the description never explains studyRef — accepted format, whether it is an ID or slug, or where to obtain one. With a single self-descriptive parameter name the gap is modest, but the description does nothing to compensate for the missing schema documentation.

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

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Generates evidence-grounded findings' from a 'Study that has responses'), which an agent can distinguish from read-only siblings like studies_get or studies_get_evidence. It does not explicitly name those siblings, so the differentiation is inferred rather than stated.

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?

'Use this when the user wants draft findings from a Study that has responses' gives a clear trigger condition, and the no-op clause ('does nothing when current findings already cover the latest evidence') defines a when-not condition. It stops short of naming alternative tools such as studies_get_evidence for examination-only cases.

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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