Zentrik

opportunities_get

Read-onlyIdempotent

Get one opportunity by its internal id or its human-readable publicId (e.g. "OPPORTUNITY-14") for the authenticated workspace. Relations are returned bounded: insightIds/ideaIds are a capped preview and relationCounts gives the true totals. Pass include: ["analysis"] to add the prioritization analysis: analysis.scores holds normalized 0..1 numbers — impact, frequency, severity, strategicAlignment, evidenceStrength, accountWeight, effort, and the composite expansionScore — this is the rankable impact×feasibility axis (higher impact-side scores = more impact; higher effort = less feasible). analysis.qualitativeImpact/qualitativeFeasibility are freeform prose context (not numeric). analysis.okrImpacts lists per-objective/key-result impact resolved to names. analysis.narrative is the "why" (problem framing, opportunity value, evidence) for justification questions. Fields are null when the opportunity has not been analyzed yet — say so rather than inferring.

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": {
      "id": "<id>"
    },
    "name": "opportunities_get"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	Internal UUID or the human-readable publicId, e.g. "OPPORTUNITY-14".
`include`	No

Output Schema

Name	Required	Description	Default
`item`	Yes

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: -[
-  "id",
-  "llm_model"
-]New value: +[
+  "id"
+]

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: -[
-  "id"
-]New value: +[
+  "id",
+  "llm_model"
+]

First observedSep 21, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already cover read-only/idempotent safety, and the description adds genuinely non-obvious behavior: relations are a capped preview while relationCounts holds true totals, and unanalyzed opportunities return nulls rather than fabricated values. That null-handling instruction is exactly the kind of caveat annotations cannot express.

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?

Front-loaded with the core action and identifier formats, then layers the analysis detail. It is dense and long for a single block, but nearly every sentence carries field-level meaning an agent needs; only the enumerated score list is mildly verbose.

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 a 2-parameter read with an output schema, the description covers the remaining gaps: identifier formats, the include opt-in, bounded relations, and null semantics. An agent has everything needed to call it correctly and interpret the analysis payload without guessing.

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

Parameters5/5

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

Schema coverage is only 50% — the include parameter has an enum but no description in the schema — and the description fully compensates by spelling out the entire structure and meaning of analysis.scores, qualitative fields, okrImpacts, and narrative. It adds substantial meaning beyond what the schema provides for the undocumented half.

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?

Opens with a specific verb+resource ("Get one opportunity") and immediately disambiguates the two accepted identifiers (internal id vs. publicId with an example). Clearly distinct from the sibling list/create/update/delete opportunity tools, which an agent can tell apart without opening a schema.

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?

Gives concrete conditional guidance: pass include:["analysis"] when prioritization data is needed, and "say so rather than inferring" when analysis fields are null. It stops short of explicitly naming when to prefer opportunities_list or opportunities_get_context instead, so the routing is clear but not fully exclusionary.

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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