Skip to main content
Glama

vep_region

Predict variant consequences for genomic regions using Variant Effect Predictor (VEP), returning transcript effects, protein changes, and regulatory impacts.

Instructions

Predict variant consequences for a genomic region using the Variant Effect Predictor (VEP). Returns detailed consequence predictions including transcript effects, protein changes, and regulatory impacts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
speciesNoSpecies name (e.g., human, mouse)human
regionYesGenomic region in format chr:start-end:strand (e.g., 9:22125503-22125502:1)
alleleYesVariant allele (e.g., C, T, DUP, DEL)
canonicalNoUse only canonical transcripts
ccdsNoUse CCDS transcripts
gencode_basicNoUse GENCODE basic transcripts
maneNoUse MANE (Matched Annotation from NCBI and EBI) transcripts
refseqNoUse RefSeq transcripts
mergedNoUse merged Ensembl/RefSeq transcripts
pickNoPick one consequence per variant
pick_alleleNoPick one consequence per variant allele
pick_allele_geneNoPick one consequence per variant allele and gene
per_geneNoUse per-gene output
hgvsNoInclude HGVS nomenclature
proteinNoInclude protein sequence
domainsNoInclude protein domains
uniprotNoInclude UniProt identifiers
tslNoInclude transcript support level
apprisNoInclude APPRIS annotation
numbersNoInclude affected exon/intron numbers
variant_classNoInclude Sequence Ontology variant class
AlphaMissenseNoInclude AlphaMissense pathogenicity predictions
CADDNoInclude CADD pathogenicity scores
REVELNoInclude REVEL pathogenicity scores
ConservationNoInclude conservation scores
LoFNoInclude Loss of Function predictions
SpliceAINoInclude SpliceAI splice-altering predictions
distanceNoDistance to transcript (bp) for upstream/downstream variants
minimalNoReturn minimal output
shift_3primeNoShift variants to 3' end if possible
transcript_versionNoInclude transcript version numbers
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 mentions the tool returns 'detailed consequence predictions' but lacks critical behavioral details such as computational cost, rate limits, authentication requirements, error handling, or whether it's a read-only or mutating operation. For a complex tool with 31 parameters, this is a significant gap in transparency.

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 appropriately sized with two sentences: the first states the purpose and tool, the second specifies the return content. It's front-loaded with the core function and avoids unnecessary details. However, it could be slightly more structured by explicitly separating purpose from output details.

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

Completeness2/5

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

Given the tool's complexity (31 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like performance, limitations, or error cases, and lacks output format details despite no output schema. For a prediction tool with many options, more context on usage and results is needed.

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 description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain parameter interactions, default behaviors, or provide examples. However, since schema coverage is high, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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 clearly states the specific action ('Predict variant consequences'), target resource ('for a genomic region'), and tool used ('using the Variant Effect Predictor (VEP)'). It distinguishes itself from sibling tools like 'get_variants_for_region' by focusing on consequence prediction rather than variant retrieval, and from 'overlap_region' by specifying VEP analysis.

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?

No explicit guidance on when to use this tool versus alternatives is provided. The description mentions what it does but doesn't indicate scenarios where it's preferred over sibling tools like 'get_variants_for_region' or 'overlap_region', nor does it mention prerequisites or exclusions. Usage is implied rather than explicitly stated.

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/munch-group/ensembl-mcp'

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