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MCPymol

by chemrich
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conservation_view

Colors protein structures by evolutionary conservation using Shannon entropy. Highlights conserved (magenta/blue) and variable (cyan/green) regions from sequence alignment.

Instructions

Colors the structure by evolutionary conservation using Shannon entropy.

Runs a full pipeline: extracts the protein sequence from the loaded structure, submits it to an MMseqs2 server (ColabFold public API by default) for multiple sequence alignment, computes per-residue Shannon entropy, and maps the conservation scores onto the structure via the B-factor column and spectrum coloring.

Entropy scores are cached in memory by sequence, so changing the scale or re-running on the same protein does not require a repeat API call.

Magenta/blue = highly conserved (low entropy), white = moderate, cyan/green = highly variable (high entropy).

NOTE: The first call makes an external API call and may take 30 seconds to several minutes depending on the server and sequence length. Subsequent calls for the same sequence are instant.

Args: obj_name: PyMOL object name (e.g. "1ubq") selection: PyMOL selection to analyze (default "all") server_url: Override the MMseqs2 server URL (defaults to ColabFold public API, or MCPYMOL_MMSEQS_URL env var) use_env: Search environmental databases in addition to UniRef (default True, gives deeper MSAs) chain: Specific chain ID to analyze. If None, uses the first protein chain found. scale: Color scaling mode. "relative" (default) maps the color gradient to the actual min/max entropy range of this protein, maximizing visual contrast. "absolute" uses the full theoretical entropy range (0 to log2(20)), useful when comparing conservation across different proteins. force_refresh: If True, bypass the cache and re-fetch the MSA from the MMseqs2 server even if scores are cached.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNo
scaleNorelative
use_envNo
obj_nameYes
selectionNoall
server_urlNo
force_refreshNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description carries full burden. It transparently discloses external API calls, caching, time estimates, color mapping meaning, scale modes, and force_refresh option. All behavioral traits are well covered.

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 well-structured with clear paragraphs: purpose/pipeline, caching, color mapping, performance note, and an Args list. It is appropriately sized, front-loaded, and every sentence adds value without redundancy.

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

Completeness5/5

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

Given the complexity (external API, caching, multiple parameters) and presence of an output schema, the description is complete: it explains the visual output, parameter options, behavioral nuances, and performance expectations. No critical gaps remain.

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

Parameters5/5

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

The 'Args' section describes each parameter in plain language, adding significant meaning beyond the schema property names. For example, 'scale: Color scaling mode. "relative" (default) maps the color gradient to the actual min/max entropy range...' This compensates for the 0% schema description coverage.

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

Purpose5/5

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

The description explicitly states the tool colors the structure by evolutionary conservation using Shannon entropy. It specifies the verb (colors), resource (structure), and method (Shannon entropy), distinguishing it from other coloring tools like bfactor_view or electrostatic_view.

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

Usage Guidelines3/5

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

The description explains the pipeline and caching behavior but does not explicitly state when to use this tool over alternatives. It implies use for conservation analysis but lacks clear when-not-to-use or comparisons with sibling tools like mutation_view.

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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