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by Nour-elhaq
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Python
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lammps_nematic_vs_time

Compute time-dependent nematic alignment from quaternion frames to track orientational ordering in molecular dynamics trajectories.

Instructions

Compute nematic alignment S(t) over a trajectory.

Each frame contains quaternions for all particles at that timestep.

Args: quaternions_frames_json: JSON string — list of (N, 4) arrays, one per frame. Example: "[[[1,0,0,0],[0.9,0.1,0,0]], [[0.8,0.2,0.1,0],[0.7,0.3,0,0]]]"

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": {
      "quaternions_frames_json": "<quaternions_frames_json>"
    },
    "name": "lammps_nematic_vs_time"
  }
}

Input Schema

NameRequiredDescriptionDefault
quaternions_frames_jsonYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

Does 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. It explains the input format (quaternions per frame) but does not disclose how the nematic alignment is computed (e.g., averaging, normalization), any constraints (e.g., equal particle counts per frame), or the output structure. The behavior remains largely opaque.

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 concise and well-structured: a one-sentence summary, a clarifying note about frame contents, and an arg definition with an example. Every sentence contributes value, and there is no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a single parameter, which is thoroughly explained in the description. The presence of an output schema removes the need to document return values. However, the description does not mention potential pitfalls (e.g., frame length consistency) or the physical meaning of S(t), so it is not fully complete for a user unfamiliar with nematic order parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides only a type and title with 0% schema description coverage, so the description must compensate. It does so effectively by explaining that 'quaternions_frames_json' is a 'JSON string — list of (N, 4) arrays, one per frame' and providing a concrete example. This adds meaningful context beyond the schema, though it could be more explicit about quaternion normalization.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action with a clear resource: 'Compute nematic alignment S(t) over a trajectory.' This distinguishes it as a time-dependent computation. However, it does not explicitly differentiate from sibling tools like lammps_nematic_order or lammps_nematic_vs_z, so it earns a 4 rather than a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, expected input preprocessing, or exclusions. Usage is only implied by the tool name and the description's focus on time-dependent trajectories.

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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