fem_modal_resultsExtract natural frequencies and mode shapes from a completed modal analysis by providing the analysis or job ID.
Extract natural frequencies from a completed modal run.
Pass analysis, or the job_id of a finished fem_run_submit solve.
Returns {frequencies_hz: [...], modes: [{mode, frequency_hz, max_displacement_mm}, ...]}.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | No | ||
| analysis | No |
Changes observed during successful MCP inspections.
v0.5.5Input schema / properties / analysis / anyOfAdded value: +[
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+]Input schema / properties / analysis / defaultAdded value: +nullInput schema / properties / analysis / typeRemoved value: -"string"Input schema / properties / job_idAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "title": "Job Id"
+}Input schema / requiredRemoved value: -[
- "analysis"
-]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true, the safety profile is already clear, and the description matches by saying 'extract' rather than mutate. It adds useful behavioral context by requiring a completed/finished solve and by spelling out the return shape, which is not present in annotations.
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?
Three sentences deliver purpose, usage, and return format with no filler. The description is front-loaded with the core action, and every sentence contributes essential information.
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?
There is no output schema, so the explicit return structure is valuable and largely completes the picture. The description is slightly incomplete in not stating whether at least one parameter is required and what happens if neither is valid, but the core workflow is well covered.
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 0%, so the description carries the burden for parameters. It clarifies that analysis and job_id are alternative identifiers and that job_id refers to a finished fem_run_submit solve. This goes beyond the bare schema, though it does not define parameter formats or precedence.
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 specific action and resource: "Extract natural frequencies from a completed modal run." The verb 'extract' plus the resource 'natural frequencies' clearly differentiates this from submission tools like fem_modal and from other result tools like fem_buckling_results or fem_thermal_results.
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 sets a clear condition: use it only on a completed modal run or a finished fem_run_submit solve. It explains which identifiers to pass (analysis or job_id), but it does not explicitly name alternative result tools or state when not to use them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.