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smaniches
by smaniches

Find Evolutionary Structural Shifts

find_evolutionary_structural_shifts
Read-only

Quantify cross-species structural divergence in a gene using AlphaFold TDA fingerprints, with fallback to sequence identity for orthologs lacking models.

Instructions

Quantify cross-species structural and sequence divergence for a gene.

For each ortholog, attempts to fetch the AlphaFold structure and compute a TDA fingerprint distance against the human structure. When an ortholog structure is available in AlphaFold DB, the divergence_method is tda_fingerprint and the distance is the L2 distance between length-normalised fingerprint vectors. When the ortholog has no AlphaFold model, the method falls back to sequence_identity (1 - identity/100).

AlphaFold DB coverage of non-human proteomes is partial: model organisms (mouse, rat, zebrafish) are well-covered; others may not be. The divergence_method field on each result tells you which method was used.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description goes beyond annotations by detailing the fallback mechanism (tda_fingerprint vs sequence_identity), the partial coverage of AlphaFold DB, and the output field divergence_method. This provides rich behavioral insight that annotations alone (readOnlyHint, openWorldHint) do not cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with an initial clear sentence, then details about methodology and caveats. It is informative but slightly verbose; some sentences could be tightened without losing clarity.

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 tool's complexity (cross-species, two methods, fallback) and the presence of an output schema and annotations, the description covers all essential aspects: purpose, methodology, coverage caveats, and result interpretation. No critical information is missing.

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

Parameters3/5

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

The input schema already provides descriptions for both parameters (gene_symbol, target_species). The tool description adds context about the overall process but does not elaborate on the parameters themselves, offering minimal added meaning beyond the schema.

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 starts with a clear verb and resource: 'Quantify cross-species structural and sequence divergence for a gene.' It goes into specific methods (TDA fingerprint distance, sequence identity), distinguishing it from sibling tools that focus on single-protein analysis or other aspects.

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 does not explicitly state when to use this tool versus alternatives. It explains the process and coverage limitations but lacks direct guidance on when a user should choose this over similar tools like compute_topology_fingerprint or compare_proteins_topologically.

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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