CovaSyn Chemistry Tools

covabio_immunogenicity

Screen protein sequence for potential T-cell epitopes (MHC-II binding). Scans 9-mer windows against 8 common HLA-DRB1 alleles using positional scoring matrices. A window counts as an epitope when it clears an absolute, background-calibrated score threshold (each allele flags at most 5% of random 9-mers of natural composition), so epitope_density is comparable between sequences: a sequence of random natural composition scores ~0.09 against this panel. Deimmunization suggestions target the P1/P4/P6/P9 anchor positions that drive binding. This is an in-silico binding prediction from hand-curated matrices, NOT an ADA rate and not calibrated against measured affinities.

Parameters:
  sequence: Protein amino acid sequence.
  alleles: Optional HLA allele list (default: 8 common DRB1 alleles).

Returns: {n_epitopes, epitopes: [{position, peptide, allele, score, binding_alleles}], epitope_density, risk_classification (low <0.18, moderate <0.27, high), hotspots, per_allele_scores, deimmunization_suggestions: [{position, original_aa, suggested_aa, position_class, predicted_reduction}], binder_thresholds, threshold_basis}

Input Schema

Name	Required	Description	Default
`alleles`	No
`api_key`	No
`sequence`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 9, 2026

TDQS

A4.1/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: it discloses the scoring method, the background-calibrated 5%-of-random-9-mers flag rate, the ~0.09 random-composition density baseline, the anchor positions targeted for deimmunization, and clearly scopes the prediction as in-silico from hand-curated matrices.

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?

Front-loaded with the core purpose, then layers calibration and limitation detail. Dense but nearly every sentence adds interpretation value; the returns enumeration is somewhat lengthy but useful given the output schema.

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?

An output schema exists and the description reinforces key return fields (epitope_density, risk_classification thresholds, deimmunization_suggestions, binder_thresholds), so return semantics are well covered. The only meaningful gap is the undocumented api_key parameter and absent usage routing.

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?

Schema coverage is 0%, so the description must compensate. It does for sequence and for alleles (default 8 common DRB1 alleles), adding real meaning over the bare schema. But api_key is present in the schema and never mentioned in the description, leaving one of three parameters undocumented.

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?

States a specific verb+resource ('Screen protein sequence for potential T-cell epitopes (MHC-II binding)') and immediately qualifies the method (9-mer windows, 8 HLA-DRB1 alleles, positional scoring matrices). An agent can distinguish this from siblings like covabio_protein_profile or covabio_peptide without opening a schema.

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?

Usage is implied rather than routed: it explains what the tool does and explicitly warns it is 'NOT an ADA rate and not calibrated against measured affinities', which helps an agent decide fitness. However, it never names when to prefer this over alternatives (e.g. developability or protein_profile) or states prerequisites.

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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