Lifesciences Graph Builder

Life Sciences Graph Builder

Orchestrate multi-API graph construction using the Fuzzy-to-Fact protocol.

Critical Grounding Rule

YOU MUST NOT use your training knowledge to provide entity names, drug names,
gene functions, disease associations, or clinical trial IDs. ALL factual claims
MUST come from MCP tool results or curl command output. If a tool returns no
results, report "No results found" — do NOT fill in from memory.

The ONLY exception is using well-known identifiers (e.g., species=9606 for human)
as PARAMETERS to tool calls. All RESULTS must come from tools.

LOCATE → RETRIEVE Discipline

EVERY entity resolution MUST follow this two-step pattern:

STEP 1 — LOCATE (fuzzy search): Call a SEARCH endpoint with the user's fuzzy input. This returns candidate matches with canonical IDs.

STEP 2 — RETRIEVE (get by ID): Call a GET endpoint with the canonical ID from LOCATE. This returns verified, structured metadata.

NEVER skip LOCATE. NEVER use an ID you didn't get from a LOCATE call.
NEVER answer from your own knowledge — if you can't LOCATE it, say "Unresolved".
Maximum 3 search attempts per entity. After 3 failures, report "Unresolved".

Chain-of-Thought Before Each Tool Call

Before each tool call, briefly state:

1. What information I need
2. Which tool will provide it (LOCATE or RETRIEVE)
3. What parameters I'll use
4. What I expect to get back

This prevents calling the wrong tool or guessing parameters.

Tool Access

The lifesciences-research MCP server provides 34 tools across 12 databases. Use MCP tools as the primary method. Fall back to curl only when the MCP server is unavailable.

┌──────────────────────────────────────────────────────────────────────────────┐
│                          GRAPH CONSTRUCTION KIT                               │
│         34 MCP tools via lifesciences-research MCP server (PRIMARY)           │
├──────────────────────────────────────────────────────────────────────────────┤
│  TIER 1: GENE/PROTEIN FOUNDATION                                             │
│  ├── HGNC: hgnc_search_genes (LOCATE), hgnc_get_gene (RETRIEVE)             │
│  ├── UniProt: uniprot_search_proteins (LOCATE), uniprot_get_protein          │
│  ├── STRING: string_search_proteins (LOCATE), string_get_interactions,       │
│  │           string_get_network_image_url                                     │
│  └── BioGRID: biogrid_search_genes (LOCATE), biogrid_get_interactions        │
├──────────────────────────────────────────────────────────────────────────────┤
│  TIER 2: DRUG DISCOVERY                                                       │
│  ├── ChEMBL: chembl_search_compounds (LOCATE), chembl_get_compound,          │
│  │           chembl_get_compounds_batch                                       │
│  ├── Open Targets: opentargets_search_targets (LOCATE),                      │
│  │           opentargets_get_target, opentargets_get_associations             │
│  ├── PubChem: pubchem_search_compounds (LOCATE), pubchem_get_compound        │
│  └── IUPHAR: iuphar_search_ligands, iuphar_get_ligand,                      │
│              iuphar_search_targets, iuphar_get_target                         │
├──────────────────────────────────────────────────────────────────────────────┤
│  TIER 3: PATHWAYS & CLINICAL                                                  │
│  ├── WikiPathways: wikipathways_search_pathways,                             │
│  │           wikipathways_get_pathway, wikipathways_get_pathways_for_gene,    │
│  │           wikipathways_get_pathway_components                              │
│  └── ClinicalTrials: clinicaltrials_search_trials (LOCATE),                  │
│              clinicaltrials_get_trial, clinicaltrials_get_trial_locations     │
├──────────────────────────────────────────────────────────────────────────────┤
│  TIER 4: GENOMICS & IDENTIFIERS                                               │
│  ├── Ensembl: ensembl_search_genes (LOCATE), ensembl_get_gene,               │
│  │            ensembl_get_transcript                                          │
│  └── Entrez: entrez_search_genes (LOCATE), entrez_get_gene,                  │
│              entrez_get_pubmed_links                                          │
├──────────────────────────────────────────────────────────────────────────────┤
│  DIRECT API (curl — for GraphQL & edge discovery when MCP unavailable)        │
│  ├── Open Targets GraphQL: knownDrugs, associatedDiseases, tractability      │
│  ├── ChEMBL /mechanism: Drug → Target edges                                  │
│  └── STRING /enrichment: Protein Set → GO/KEGG terms                         │
├──────────────────────────────────────────────────────────────────────────────┤
│  GRAPHITI (Persistence)                                                       │
│  └── persist_to_graphiti: Save validated subgraph as JSON episode             │
└──────────────────────────────────────────────────────────────────────────────┘

MCP Token Budgeting (slim Parameter)

ALL 34 MCP tools support a slim parameter for phase-specific token budgeting:

Phase-Specific Usage:

• LOCATE phases (1 ANCHOR, 3 EXPAND): Use slim=true, page_size=3-5 for fast candidate lists
• RETRIEVE phases (2 ENRICH, 4 TRAVERSE, 5 VALIDATE): Use slim=false (default) for full metadata

Token Savings:

• slim=true: ~20 tokens/entity (ID, symbol, name only)
• slim=false: ~115-300 tokens/entity (full metadata + cross-references)

Example (Phase 1 ANCHOR):

# LOCATE: Find gene candidates with slim=true
Call `hgnc_search_genes` with: {"query": "ACVR", "slim": true, "page_size": 5}
→ Returns 5 candidates at ~20 tokens each = 100 tokens total

# RETRIEVE: Get full record for selected candidate
Call `hgnc_get_gene` with: {"hgnc_id": "HGNC:171"}
→ Returns complete metadata (~115 tokens)

Impact: Using slim=true during LOCATE phases enables batch resolution of 10-50 entities per LLM turn, critical for network expansion (Phase 3) and drug discovery (Phase 4a).

Reference: Token budgeting pattern documented in reference/prior-art-api-patterns.md (Section 7.1).

CURIE Format Conventions

Two contexts require different ID formats:

API Arguments (bare IDs)

Use bare IDs matching what the MCP server or API accepts:

• "Q04771" for UniProt
• "CHEMBL25" for ChEMBL compounds
• "9606.ENSP00000269305" for STRING proteins
• "ENSG00000141510" for Ensembl genes
• "HGNC:11998" for HGNC (includes prefix — this is how HGNC API works)

Graph Node IDs (full CURIEs for Graphiti)

Use PREFIX:LOCAL_ID format for persistence:

• "HGNC:11998" for genes
• "UniProtKB:P04637" for proteins
• "CHEMBL:3137309" for compounds
• "STRING:9606.ENSP00000269305" for STRING proteins
• "NCT03312634" for trials (no prefix needed — NCT is the standard)
• "EFO:0000574" or "MONDO:0018875" for diseases
• "WP:WP1742" for pathways

Fuzzy-to-Fact Execution Checklist

• Phase 1 ANCHOR: LOCATE gene/drug/disease → RETRIEVE canonical CURIEs
• Phase 2 ENRICH: RETRIEVE metadata for each CURIE (UniProt function, cross-refs)
• Phase 3 EXPAND: LOCATE interaction partners → RETRIEVE pathway membership
• Phase 4a TRAVERSE_DRUGS: LOCATE drugs targeting identified proteins → RETRIEVE mechanisms
• Phase 4b TRAVERSE_TRIALS: LOCATE trials for identified drugs → RETRIEVE trial details
• Phase 5 VALIDATE: RETRIEVE verification for every NCT ID, drug mechanism, gene-disease link
• Phase 6a PERSIST: Format validated graph as JSON → persist to Graphiti
• Phase 6b REPORT: Select template → grade evidence → format final report (lifesciences-reporting skill)

Phase 1: ANCHOR — Entity Resolution

Goal: Resolve every gene, drug, and disease in the user's query to canonical CURIEs.

LOCATE (always start with HGNC for genes — fastest, most reliable):

PRIMARY (MCP tool):

Call `hgnc_search_genes` with: {"query": "TP53"}
→ Returns candidates with HGNC IDs → select best match: HGNC:11998

FALLBACK (curl):

curl -s "https://rest.genenames.org/search/symbol/TP53" \
  -H "Accept: application/json" | jq '.response.docs[0] | {hgnc_id, symbol, name}'

RETRIEVE (get full record with cross-references):

PRIMARY (MCP tool):

Call `hgnc_get_gene` with: {"hgnc_id": "HGNC:11998"}
→ Returns: symbol, name, UniProt cross-ref, Ensembl cross-ref, Entrez cross-ref

FALLBACK (curl):

curl -s "https://rest.genenames.org/fetch/hgnc_id/11998" \
  -H "Accept: application/json" | jq '.response.docs[0]'

For drugs (try ChEMBL search, note failure for Phase 4a fallback):

PRIMARY (MCP tool):

Call `chembl_search_compounds` with: {"query": "Venetoclax"}
→ If 500 error: note it, move on — drugs resolved in Phase 4a via Open Targets

For diseases (use ClinicalTrials.gov or Open Targets search):

PRIMARY (MCP tool):

Call `clinicaltrials_search_trials` with: {"query": "fibrodysplasia ossificans progressiva"}

Output: JSON with resolved entities:

{
  "entities": [
    {"mention": "TP53", "type": "Gene", "curie": "HGNC:11998", "symbol": "TP53", "status": "resolved"},
    {"mention": "FOP", "type": "Disease", "curie": "MONDO:0018875", "status": "resolved"}
  ],
  "unresolved": []
}

Phase 2: ENRICH — Metadata Enrichment

Goal: Decorate each resolved entity with metadata and cross-references. UniProt function text is the most valuable output.

RETRIEVE gene metadata (captures cross-references for downstream phases):

PRIMARY (MCP tool):

Call `hgnc_get_gene` with: {"hgnc_id": "HGNC:11998"}
→ Extract: uniprot_id="P04637", ensembl_id="ENSG00000141510", entrez_id="7157"

RETRIEVE protein function (reveals interactors, pathways, disease connections):

PRIMARY (MCP tool):

Call `uniprot_get_protein` with: {"uniprot_id": "P04637"}
→ Parse function text for interactor mentions: BAX, BCL2, FAS, MDM2

RETRIEVE drug metadata (if drug was resolved in Phase 1):

PRIMARY (MCP tool):

Call `chembl_get_compound` with: {"chembl_id": "CHEMBL3137309"}
→ May return 500 — note failure, continue

LOCATE disease CURIE (use Ensembl ID from HGNC cross-references above):

PRIMARY (MCP tool):

Call `opentargets_get_associations` with: {"ensembl_id": "ENSG00000115170"}
→ Returns diseases with MONDO/EFO/Orphanet IDs + association scores
→ Pick the highest-scoring disease matching the user's query
→ Record disease CURIE (e.g., MONDO:0007606 for FOP) for Phase 4a/4b

Disease CURIE Optionality:

• REQUIRED if drug discovery (Phase 4a) or clinical trial search (Phase 4b) is in scope
• OPTIONAL for gene network questions (Template 2) without therapeutic focus
• If query is about biological mechanisms (not therapeutics), disease CURIE may be omitted

FALLBACK (curl):

curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \
  -H "Content-Type: application/json" \
  -d '{"query": "{ target(ensemblId: \"ENSG00000115170\") { associatedDiseases(page: {index: 0, size: 5}) { rows { disease { name id } score } } } }"}'

Critical outputs for downstream phases:

• Ensembl ID (ENSG...) → needed by Phase 4a for Open Targets GraphQL
• UniProt ID → needed by Phase 3 for STRING
• Interactor mentions from function text → guides Phase 3 expansion
• Disease CURIE (MONDO/EFO ID) → needed by Phase 4a/4b for drug/trial filtering (OPTIONAL if Phases 4a/4b not in scope)

Phase 3: EXPAND — Network Expansion

Goal: Build adjacency list from interaction databases.

LOCATE STRING protein ID:

PRIMARY (MCP tool):

Call `string_search_proteins` with: {"query": "TP53", "species": 9606}
→ Returns: 9606.ENSP00000269305

RETRIEVE protein interactions:

PRIMARY (MCP tool):

Call `string_get_interactions` with: {"string_id": "9606.ENSP00000269305", "species": 9606, "required_score": 700}
→ Returns: MDM2 (0.999), SIRT1 (0.999), ATM (0.995), BCL2

RETRIEVE pathway membership:

PRIMARY (MCP tool):

Call `wikipathways_get_pathways_for_gene` with: {"gene_id": "TP53"}

RETRIEVE gene-disease associations (Open Targets):

PRIMARY (MCP tool):

Call `opentargets_get_associations` with: {"ensembl_id": "ENSG00000141510"}
→ Returns associated diseases with scores

FALLBACK (curl — for custom GraphQL queries):

curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \
  -H "Content-Type: application/json" \
  -d '{"query": "{ target(ensemblId: \"ENSG00000141510\") { associatedDiseases(page: {index: 0, size: 5}) { rows { disease { name id } score } } } }"}'

Pitfalls:

• STRING batch queries (multiple proteins) return protein names; single queries may NOT. Prefer batch.
• STRING rate limit is 1 req/s.
• Always use species=9606 (human) unless explicitly doing comparative genomics.
• BioGRID requires BIOGRID_API_KEY — check with grep BIOGRID_API_KEY .env.

Phase 4a: TRAVERSE_DRUGS — Drug Discovery

Goal: Find drugs targeting identified proteins. Open Targets is the PRIMARY source (more reliable than ChEMBL).

LOCATE drugs via Open Targets (preferred — returns drugs + mechanisms + phases in one call):

PRIMARY (MCP tool):

Call `opentargets_get_target` with: {"ensembl_id": "ENSG00000171791"}
→ Returns knownDrugs with drug name, mechanismOfAction, phase

FALLBACK (curl — for full GraphQL control):

curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \
  -H "Content-Type: application/json" \
  -d '{"query": "{ target(ensemblId: \"ENSG00000171791\") { knownDrugs(size: 25) { rows { drug { name id } mechanismOfAction phase } } } }"}'

Open Targets knownDrugs Pagination:

• Use size parameter only (e.g., size: 25) — this is the reliable pattern
• Do NOT use page or index — these cause intermittent failures
• For paginated results, use cursor (returned in the response) as the continuation token
• If first query fails, retry with size only (no other pagination params)

LOCATE drugs via ChEMBL (secondary fallback — frequently 500s on detail endpoints):

PRIMARY (MCP tool):

Call `chembl_search_compounds` with: {"query": "BCL2 inhibitor"}
→ Search endpoint is generally reliable

Gain-of-Function Disease Filter (CRITICAL for diseases like FOP):

For GAIN-OF-FUNCTION diseases (e.g., FOP caused by constitutive ACVR1 activation),
you need INHIBITORS or ANTAGONISTS. Do NOT return agonists — they worsen the disease.

Check the "mechanismOfAction" field from Open Targets:
- INCLUDE: "inhibitor", "antagonist", "negative modulator", "antibody (blocking)"
- EXCLUDE: "agonist", "positive modulator", "activator"

Pitfalls:

• Open Targets GraphQL knownDrugs: use size only (e.g., size: 25). Do NOT use page/index. Use cursor for pagination.
• The Ensembl ID (ENSG...) is required for Open Targets target queries — get from Phase 2.
• ChEMBL detail endpoints (chembl_get_compound) often return 500 errors; search endpoints (chembl_search_compounds) are generally reliable.
• Do NOT retry ChEMBL more than once — switch to Open Targets.

Phase 4b: TRAVERSE_TRIALS — Clinical Trial Discovery

Goal: Find clinical trials for identified drugs. Can run in parallel with Phase 4a.

LOCATE trials by drug + disease:

PRIMARY (MCP tool):

Call `clinicaltrials_search_trials` with: {"query": "venetoclax AND leukemia"}
→ Returns trials with NCT IDs, phases, statuses

FALLBACK (curl):

curl -s "https://clinicaltrials.gov/api/v2/studies?query.cond=cancer&query.intr=venetoclax&pageSize=5&format=json" \
  | jq '.studies[] | {nct: .protocolSection.identificationModule.nctId, title: .protocolSection.identificationModule.briefTitle, phase: .protocolSection.designModule.phases, status: .protocolSection.statusModule.overallStatus}'

Fallback — disease-only search (when drug-specific search returns zero):

curl -s "https://clinicaltrials.gov/api/v2/studies?query.cond=fibrodysplasia+ossificans+progressiva&pageSize=10&format=json"

ClinicalTrials.gov v2 Valid Parameters:

• query.cond — condition/disease
• query.intr — intervention/drug
• query.term — general search (supports AREA[StudyType]INTERVENTIONAL)
• filter.overallStatus — e.g., RECRUITING, COMPLETED
• pageSize — results per page
• format — json

Invalid parameter: filter.studyType does NOT exist in v2 API. Use query.term=AREA[StudyType]INTERVENTIONAL instead.

Pitfalls:

• Use SPECIFIC drug names from Phase 4a output, NOT broad terms like "inhibitor".
• Search for each drug separately to avoid missing trials.
• If drug-specific search returns zero, try disease-only search as fallback.

Phase 5: VALIDATE — Fact Verification

Goal: Verify every NCT ID, drug mechanism, and gene-disease claim. Prevents hallucinations.

RETRIEVE trial verification:

PRIMARY (MCP tool):

Call `clinicaltrials_get_trial` with: {"nct_id": "NCT03312634"}
→ If "Entity Not Found" → mark as INVALID

RETRIEVE cross-database ID verification:

FALLBACK (curl — for Ensembl xrefs):

curl -s "https://rest.ensembl.org/xrefs/id/ENSG00000141510?content-type=application/json" \
  | jq '.[] | select(.dbname | test("HGNC|UniProt|OMIM|RefSeq")) | {db: .dbname, id: .primary_id}'

Validation checklist:

• Every NCT ID verified via clinicaltrials_get_trial
• Drug mechanisms match what Open Targets/ChEMBL reported
• Gene-protein ID mappings consistent across HGNC, UniProt, Ensembl
• No entity was introduced from parametric knowledge (everything traces to a tool call)

Verdicts: Mark each fact as VALIDATED, INVALID (with reason), or UNVERIFIABLE.

Phase 6a: PERSIST — Graph Persistence

Goal: Format validated graph as JSON and persist to Graphiti.

Structure (only include VALIDATED entities and relationships):

graph_data = {
    "nodes": [
        {"id": "HGNC:11998", "type": "Gene", "label": "TP53", "properties": {"ensembl": "ENSG00000141510"}},
        {"id": "HGNC:990", "type": "Gene", "label": "BCL2", "properties": {"ensembl": "ENSG00000171791"}},
        {"id": "CHEMBL:3137309", "type": "Compound", "label": "Venetoclax", "properties": {"phase": 4}}
    ],
    "edges": [
        {"source": "HGNC:11998", "target": "HGNC:990", "type": "REGULATES", "properties": {}},
        {"source": "CHEMBL:3137309", "target": "HGNC:990", "type": "INHIBITOR", "properties": {"mechanism": "BCL2 inhibitor"}}
    ]
}

Persist (if Graphiti is available):

persist_to_graphiti(
    name="TP53-BCL2-Venetoclax pathway",
    episode_body=json.dumps(graph_data),
    source="json",
    group_id="drug-repurposing"
)

Phase 6b: REPORT — Formatted Report with Evidence Grading

Goal: Produce a professional report using the lifesciences-reporting skill.

Use the lifesciences-reporting skill to format the Phases 1-5 output. The skill will:

1. Template selection: Route query through its Template Decision Tree (7 templates)
2. Evidence grading: Apply L1-L4 levels + modifiers to all claims
3. Confidence calculation: Compute median of all claim scores (resistant to outliers)
4. Source attribution: Include [Source: tool(param)] on every factual claim

The reporting skill consumes Phases 1-5 output and the graph structure from Phase 6a. It does NOT make new API calls.

Fallback (if reporting skill unavailable):

## Summary
[Direct answer with source citations on every claim]

## Confidence
[State that full evidence grading was not performed and why]

Edge Discovery Commands

These curl commands are for edge types not covered by MCP tools:

Node Types

Edge Types

API Reliability & Fallback Patterns

Query Best Practices

Human-Centric Defaults

• Default to species=9606 (human) for gene/protein searches
• Use page_size=10 for exploration, page_size=50 for batch operations
• Only omit organism filter for comparative genomics across species

Drug Discovery vs Repurposing

• Drug repurposing: Use phase >= 2 (clinical validation, shorter approval path)
• General discovery: No phase filter (include preclinical tools, mechanism probes)
• Always check mechanisms before bioactivity data

Clinical Trial Defaults

• Default filter.overallStatus=RECRUITING for active research
• No phase filter for full landscape view
• Use phase filter only for specific analysis (PHASE3+ for commercialization)

See Also

• lifesciences-genomics: HGNC, Ensembl, NCBI gene resolution endpoints (Phases 1-2)
• lifesciences-proteomics: UniProt, STRING, BioGRID interaction endpoints (Phases 2-3)
• lifesciences-pharmacology: ChEMBL, PubChem, IUPHAR, Open Targets drug endpoints (Phase 4a)
• lifesciences-clinical: Open Targets associations, ClinicalTrials.gov trial endpoints (Phases 4b, 5)
• lifesciences-crispr: BioGRID ORCS essentiality validation (extends Phase 3 with CRISPR screen data)
• lifesciences-reporting: Domain-specific report templates and evidence grading (Phase 6b)

MCP Server Reference

All 34 tools are available via the lifesciences-research MCP server. Endpoint: https://lifesciences-research.fastmcp.app/mcp

Graphiti persistence is optional/best-effort. Core workflow completion is producing validated graph and report artifacts.

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