Inspect LS-Reader results in a dedicated Python/ABI process to analyze simulation output.
Inspect a result using LS-Reader in its own configured Python/ABI process.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It adds one useful trait (execution in its own configured Python/ABI process, implying isolation) but omits whether the operation is read-only, what permissions or environment configuration are required, whether it has side effects, and what the inspection yields.
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?
It is a single short sentence with no redundant filler and the verb is front-loaded. However, the brevity comes at the cost of meaningful detail, and the phrase 'in its own configured Python/ABI process' is jargon that does not help selection.
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 an inspect tool with no output schema, no annotations, and an undocumented required parameter, the description should explain what gets inspected and what is returned. It leaves all of this unstated, making it incomplete for an agent trying to invoke it correctly.
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 description coverage is 0% and the single 'path' parameter is undocumented in both schema and description. The description does not clarify what the path points to (a result file, a directory, a model directory), so it fails to compensate for the coverage gap.
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 names a verb (inspect) and a resource (a result via LS-Reader), which is more than a tautology, but 'a result' is vague about what is being inspected and what the tool returns. It does not differentiate itself from siblings that also operate on LS-Reader or inspect result files, such as inspect_d3plot_database, inspect_binout, or extract_lsreader_nodal.
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 when-to-use, when-not-to-use, prerequisite, or alternative-selection guidance is given. The mention of 'LS-Reader in its own configured Python/ABI process' implies a specialized execution context but does not tell an agent when this is preferable to inspect_keyword_deck, inspect_model, or the extract_* siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.