Set a model variable to a value via full path, accepting scalars as strings or arrays as JSON array strings. Update model parameters directly in IC-CAP without manual editing.
Set a Model variable value. Pass scalar values as strings and arrays as JSON array strings such as '["a","b","c"]'.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | Value or JSON array string | |
| var_path | Yes | Full path |
Changes observed during successful MCP inspections.
v0.2.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Set' implies mutation, but the description doesn't state whether the variable must already exist, whether the operation overwrites or appends, what happens on an invalid var_path, or what is returned. The JSON array string note discloses input encoding behavior, which is helpful, but the mutation semantics remain opaque.
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?
Two sentences and 27 words with zero fluff. The operative statement 'Set a Model variable value' is front-loaded, and the format clarification follows naturally. Every sentence earns its place, though the example could have been omitted without losing meaning.
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?
This is a simple 2-parameter setter with no output schema and no annotations, so the description gets partial credit for covering the action and input format. However, for a mutating tool it should state whether the target variable must pre-exist and what happens on failure. An agent can call it correctly but cannot predict the outcome of edge cases.
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?
Schema coverage is 100%, so the baseline is 3, but the description adds genuinely useful format semantics the schema lacks: scalars must be passed as strings and arrays as JSON array strings, with a concrete example. An agent reading only the schema ('Value or JSON array string') wouldn't know the exact encoding rules that the description clarifies.
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 states a clear verb+resource pair: 'Set a Model variable value.' The 'Model variable' qualifier helps distinguish it from sibling setters like dvar_set, svar_set, and ivar_set, though it never explicitly names those alternatives. This is clear but stops short of explicit sibling differentiation.
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?
No guidance is given on when to use this tool versus alternatives. It doesn't mention that mvar_create should be called first to create the variable, that mvar_get reads it back, or that object_set_val is the generic fallback. The naming convention implies usage, but the description itself provides no routing or exclusion logic.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.