tentra

get_decisions_for

Retrieve architectural decisions affecting specific entities like services, files, or contracts to understand design rationale during code review.

Instructions

Retrieve all decisions that affect a specific entity (service, file, symbol, contract, or domain). Use to surface architectural rationale while reviewing code.

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": {
      "entity_type": "<entity_type>",
      "entity_id": "<entity_id>"
    },
    "name": "get_decisions_for"
  }
}

Input Schema

Name	Required	Description
`entity_type`	Yes	Type of entity to look up decisions for
`entity_id`	Yes	ID of the entity (service ID, file ID, symbol ID, etc.)
`include_superseded`	No	Whether to include decisions with status "superseded" in results (default true — full lineage)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Apr 18, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieving decisions but does not describe key behaviors such as authentication requirements, rate limits, pagination, error handling, or what the output looks like (since there's no output schema). This leaves significant gaps for an agent to understand how to interact with the tool effectively.

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 two sentences, front-loaded with the core purpose and followed by usage context. Every sentence earns its place: the first defines the tool's function, and the second provides practical application, with no wasted words or redundancy.

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?

Given the complexity of a retrieval tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., response format, error cases) and relies solely on the input schema, which doesn't cover output or operational context. This makes it insufficient for an agent to fully understand the tool's behavior and results.

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 100%, so the schema already documents all parameters well. The description adds minimal value beyond the schema by implying the tool retrieves decisions 'for a specific entity,' which aligns with the parameters but doesn't provide additional syntax, format details, or usage examples. This meets the baseline for high 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?

The description clearly states the verb ('Retrieve') and resource ('all decisions that affect a specific entity'), specifying the entity types (service, file, symbol, contract, or domain). It distinguishes this from sibling tools like 'record_decision' (which creates decisions) and 'get_architecture' (which retrieves architecture data), making the purpose specific and differentiated.

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 for when to use the tool ('to surface architectural rationale while reviewing code'), which implies it's for understanding design decisions during code review. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., 'get_architecture' for broader architectural data), leaving some guidance implicit rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.