Skip to main content
Glama

Score Binding Pocket Geometry

score_binding_pocket_geometry
Read-onlyIdempotent

Identify and score putative binding pockets in AlphaFold structures using geometry-based heuristics, evaluating druggability and pocket geometry metrics.

Instructions

Identify and score putative binding pockets from AlphaFold geometry.

Detects pockets with a geometry-only heuristic. Residues in the inner 60 percent of the structure by distance from the centroid are taken as the pocket-forming core, then grown greedily into clusters within an 8 Angstrom radius. A cluster is kept as a putative pocket when it has at least min_pocket_residues members and a mean pLDDT of at least 50.

Each pocket reports a radius of gyration (compactness of the pocket residues), a centroid offset (distance of the pocket centroid from the structure centroid; larger means more peripheral — a solvent-accessible cleft rather than a dead-central cavity, and NOT a measure of solvent burial), a mean pLDDT, and a druggability index. The druggability index runs 0 to 100 and is the sum of four equally weighted 0 to 25 sub-scores: residue count, radius of gyration, mean pLDDT, and centroid offset.

This is a fast, dependency-free pre-screen, not a substitute for a validated pocket detector such as fpocket or P2Rank. It needs no ML model, is fully reproducible from AlphaFold coordinates, and runs in air-gapped deployments.

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?

Beyond annotations (readOnlyHint, idempotentHint), the description elaborates on the detection algorithm (centroid distance, greedy clustering, pLDDT threshold), output metrics (radius of gyration, centroid offset, druggability index), and limitations. This provides comprehensive behavioral context.

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 a clear first sentence, followed by algorithm details, output metrics, and caveats. It is concise (~150 words) but packs essential information. Minor improvements could include front-loading the output metrics more directly.

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 and presence of an output schema, the description covers the algorithm, output metrics, limitations, and ideal use cases. It fully enables an agent to understand the tool's behavior and output without missing critical details.

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 description does not explicitly explain the parameters 'uniprot_id' or 'min_pocket_residues', though the latter is mentioned in the algorithm. With schema description coverage at 0%, the description could provide more parameter context, but the schema itself includes minimal descriptions. The tool's algorithm description indirectly clarifies 'min_pocket_residues' usage.

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's purpose: 'Identify and score putative binding pockets from AlphaFold geometry.' It details the heuristic method and distinguishes itself from validated detectors like fpocket or P2Rank, ensuring no confusion with sibling tools.

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

Usage Guidelines5/5

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

The description clearly specifies when to use this tool ('fast, dependency-free pre-screen') and when not to ('not a substitute for a validated pocket detector'). It also highlights ideal use cases: air-gapped deployments and scenarios needing reproducibility from AlphaFold coordinates.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/smaniches/alphafold-sovereign-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server