| name | pathway-report |
| description | Generate a pathway deep-dive report combining Reactome pathway data, member gene summaries, ClinVar variants, and PubMed literature |
Generate a comprehensive pathway deep-dive report for: $ARGUMENTS
Use the MCP tools available to you to gather data from all relevant sources, then synthesize a single structured report. Follow the steps below in order. If a step fails or returns no data, note the gap and continue — do not stop the report.
Data Gathering Steps
1. Pathway Identification
- If the input looks like a Reactome stable ID (starts with
R-), call reactome_get_pathway with pathway_id set to that ID, with include_participants: true and include_hierarchy: true.
- Otherwise, call
reactome_get_pathway with query set to the input to search for matching pathways. Present the top hits and pick the most relevant one, then do a direct lookup with include_participants: true and include_hierarchy: true.
2. Pathway Overview
From the Reactome data, extract:
- Pathway name, stable ID, and species
- Summation/description
- Position in the pathway hierarchy (parent pathways)
- Sub-events (child pathways and reactions) — list the first 10-15
- Compartments (cellular locations)
- Whether it is disease-associated
3. Key Member Genes
- From the participants list, identify the gene/protein participants (filter for UniProt/ReferenceGeneProduct entries).
- Select up to 10 key genes from the participant list.
- Call
batch_gene_summary with those gene symbols (taxon: human) to get brief summaries.
- For the top 3-5 most important genes, call
uniprot_get_protein to get function descriptions and GO terms.
4. Protein Interactions Within the Pathway
- Pick the 2-3 most central genes from the pathway.
- Call
string_get_interactions for each to see how pathway members interact. Focus on interactions between pathway members rather than external interactions.
5. Clinical Relevance
- For the top 3-5 key genes, call
clinvar_search to find pathogenic/likely pathogenic variants.
- Summarize the most clinically significant variants (up to 3 per gene, 10 total max).
- Note which diseases are associated with variants in pathway members.
6. KEGG Cross-Reference
- Call
kegg_get_pathway with the pathway name or key gene to find the equivalent KEGG pathway (if any).
- Note any additional member genes or connections found in KEGG but not Reactome.
7. Literature
- Call
pubmed_search with the pathway name to find recent relevant publications (limit: 10).
- If few results, also search with the names of 2-3 key member genes combined with the pathway topic.
Report Format
Present the report in this structure:
Pathway: [Name] ([Reactome ID])
Summary: [1-2 sentence pathway description]
Hierarchy: [Top-level pathway] > [Parent pathway] > [This pathway]
Compartments: [cellular locations]
Disease-associated: Yes/No
Sub-pathways & Reactions
[Numbered list of child events, noting their type (Pathway vs Reaction)]
Key Member Genes ([N] total participants)
| Gene | Full Name | Function Summary |
|---|
| [Table of top 10 genes with brief descriptions] | | |
Protein Interaction Network
[Summary of how key pathway members interact, noting interaction scores and key hubs]
Clinical Variants in Pathway Members
| Gene | Variant | Clinical Significance | Condition |
|---|
| [Table of significant ClinVar variants] | | | |
Disease associations: [Summary of diseases linked to this pathway through variant data]
KEGG Cross-Reference
[KEGG pathway ID and name if found, plus any additional insights]
Recent Literature
| # | Title | Year | Journal | PMID |
|---|
| [Table of top 5-8 relevant publications] | | | | |
Key Takeaways
[3-5 bullet points summarizing the most important findings: biological role, clinical significance, key genes, and any notable patterns]
