List every relation touching an entity, as either subject or object — the two-directional view datum_edges gives. A filtered scan over every stored relation rather than a maintained index: this is a simulation sandbox's model graph, not a large corpus, so scanning on each call is the honest tradeoff over a second data structure that could drift from the source of truth.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | entity id to look up |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says the tool only lists relations, implying a safe read operation, but the annotations mark readOnlyHint=false and destructiveHint=true. This is a direct annotation contradiction that makes the tool's actual side effects unclear and undermines the description's credibility.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with the core action front-loaded and the rationale in a second explanatory sentence. The tradeoff discussion is wordy but earns its place as performance guidance, so the length is reasonable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter list operation, the description conveys the result scope and cost model well. However, it doesn't describe the shape of the returned relations, and the contradictory annotations prevent a complete picture of the tool's safety profile, so an agent cannot fully rely on this definition.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the single id parameter as 'entity id to look up' with 100% coverage. The description adds the context that the entity lives in a model graph, but that doesn't meaningfully extend the schema's parameter semantics, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'List every relation touching an entity, as either subject or object.' It clearly differentiates the two-directional scope from the one-directional view datum_edges gives, so an agent can tell exactly what this tool returns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives useful context about when a scan is acceptable ('this is a simulation sandbox's model graph, not a large corpus') and explains the performance tradeoff of scanning over a maintained index. However, it doesn't explicitly name alternatives or state when not to use this tool over similarly named siblings like sim_neighbors.
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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