metabo-idmapper
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@metabo-idmappermap my metabolite names to KEGG IDs"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
metabo-idmapper
Turn messy metabolite names into input-usable IDs — KEGG / HMDB / ChEBI / PubChem / InChIKey, plus a Mouse-GEM MAM crosswalk for metabolic-model (flux) input — exposed as a single MCP tool registry.
Same philosophy as scpilot: a deterministic registry where every tool emits evidence
(candidate IDs, xref bridges, formula/mass verification, coverage). The connecting LLM is
the reasoning layer that makes the identity call (which candidate is correct, endogenous
vs xenobiotic, confidence tier, final inclusion). Tools never fabricate an ID; record_decision
refuses any accepted ID that no tool produced.
Code vs LLM-judgment split
Deterministic code (MCP tools) | LLM judgment (the call) |
normalize names; exact DB match; PubChem/KEGG/ChEBI search; BridgeDb xref; molmass formula/mass verify; m/z→mass windows; Mouse-GEM crosswalk; coverage | is a fuzzy/typo/synonym candidate correct? abbreviation expansion? endogenous vs xenobiotic? id-gap vs model-scope-absent? confidence tier; isomer disambiguation; final inclusion |
Related MCP server: BioBTree
Tools (20)
midmap_guidance · detect_state · ingest_names · exact_match · structure_lookup ·
search_synonym · bridge_xref · verify_candidate · mass_match_candidates ·
screen_exogenous · record_decision · backfill_hmdb · gem_crosswalk ·
mapping_provenance · annotate_source · plot_coverage · export_code ·
export_report_ppt · coverage_summary · harness_audit
Call midmap_guidance first for the canonical workflow, confidence tiers (M1–U), and gotchas.
Origin taxonomy — exogenous vs xenobiotic
record_decision resolves two axes: ID coverage (final_class) and provenance
(origin). Non-endogenous compounds are split into two distinct classes instead of one
"excluded" bucket:
final_class | origin | disposition |
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| host-produced, analysed |
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| KEPT + tagged — a real outside-host signal; keeps its KEGG/HMDB IDs and can enter the GEM crosswalk |
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| EXCLUDED — non-biological (LC-MS additive / surfactant / plasticizer / industrial) |
screen_exogenous detects only the non-biological classes deterministically (and
auto-suggests the origin); the biological-exogenous call (drug / diet / gut-microbial / plant)
is a reasoning-layer judgement. A drug or dietary metabolite is tagged exogenous and kept —
never dumped into the excluded bucket. The harness_audit origin_coherence check fails any
entry whose origin and class disagree (e.g. origin='drug' with xenobiotic-excluded).
Governance harness (harness_audit)
A read-only self-audit — the metabo-idmapper counterpart to a run harness — that makes no
identity call and changes nothing. Run it last (after coverage_summary): it reads the
ledger + emitted artifacts and checks that the reasoning layer actually honored this project's
own contract, emitting a per-check pass / warn / fail scorecard. It catches rules that were
"defined but not followed": a fabricated ID (accepted but produced by no tool), a mass-only
W-tier candidate used as primary, an incoherent final_class↔confidence pair, a fuzzy
(M2/M3) accept with no recorded formula/mass verification, a locant/anomer-sensitive name
accepted via a non-exact route and never re-checked, a xenobiotic class excluded
inconsistently, an origin↔class mismatch, a flagged trade-name auto-accept never reviewed, an
id-gap KEGG never re-tried,
a decision with no rationale, entries left pending, a skipped gem_crosswalk, or missing
stage-7 always-emit artifacts. Fix every fail; review each warn.
Report outputs (always emitted by coverage_summary)
master_ledger.tsv,coverage_summary.tsv,mapping_provenance.tsvProvenance tables (
mapping_provenance):kegg_recovered.tsv/hmdb_recovered.tsv— what was mapped BEYOND the MetaboAnalyst 1st pass, with the harmonized name, id, and the logic/route (typo fix, synonym search, xref bridge);unmapped_harmonization.tsv— structure-only entries with the names tried and why mapping failed;exogenous_kept.tsv— biological outside-host metabolites kept + tagged;xenobiotic_excluded.tsv— non-biological contaminants excluded, each with itsorigin+ full reason.PPTX report (
export_report_ppt): a slide deck built from the run artifacts — Title · Coverage KPIs · Methods · Pipeline · UpSet · Improvement · Recovery cause→fix · KEGG/HMDB recovered · Unmapped · Exogenous(kept) · Xenobiotic(excluded) · Outputs.Annotated source (
annotate_source): the ORIGINAL data file with the final ID columns appended (<source>_annotated.xlsx/.tsv: intensity matrix + ID_kegg / ID_hmdb / ID_chebi / ID_pubchem / ID_inchikey / ID_final_class / ID_origin / ID_gem_mam per compound). Auto-detects the name column; passheader=for vendor sheets with a preamble.Figures (
plot_coverage):figures/db_matching_upset.png(5-DB coverage UpSet +enriched_xref.tsv) andfigures/db_matching_improvement.png(MetaboAnalyst baseline vs current logic).Reproduction code (
export_code):code/reproduce_mapping.py(+.ipynb) — standalone reproductions using the ORIGINAL library APIs (MetaboAnalystR / BridgeDbR / KEGGREST via Rscript, PubChem PUG-REST, molmass, COBRApy, matplotlib), NOT the tool wrappers. They make the flow explicit and RUN it: Stage 1 MetaboAnalyst 1st pass → Stage 2 extract unmapped → Stage 3 re-run KEGG/PubChem searches with the harmonized names READ from the saved ledger → Stage 4 BridgeDb cross-check + HMDB backfill → GEM crosswalk → figure. The.ipynbis unrolled into linear cells (nodef) with per-cell input-source / output / reuse comments; the 3 raw R engines ship ascode/{ma,bridge,kegg}.Rsidecars.
Reasoning layer (embedded in the MCP)
The LLM reasoning layer is not an external subagent — it ships inside the MCP as two prompts and two resources, placed at the stages where judgment actually lives:
role | MCP prompt | resource | placed at |
driver — drive the tools end-to-end, own the identity CALLs |
|
| all stages (judgment in 3 & 5) |
reviewer — adversarially verify accepted identities + exclusions |
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| after |
Connect the server and invoke the map_metabolites prompt to become the driver; it invokes
review_mappings before finalizing. No config symlinks — the whole reasoning layer travels
with the server.
Engine
Python (PubChem REST, ChEBI OLS4, molmass, COBRApy) + out-of-process system R
(BridgeDbR, KEGGREST, MetaboAnalystR) via bundled rscripts/. Reuses the verified
metabolite-id-harmonization skill logic.
Install
# scientific stack lives in the conda env `cobragem`; don't let pip re-resolve it
/home/wykim/miniforge3/envs/cobragem/bin/pip install -e . --no-depsRequired external assets (overridable by env var):
env var | default |
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Run
python -m metabo_idmapper # stdio MCP serverRegister with Claude Code:
claude mcp add metabo-idmapper -- /home/wykim/miniforge3/envs/cobragem/bin/python -m metabo_idmapperTest
/home/wykim/miniforge3/envs/cobragem/bin/python -m pytest -q # 7 offline smoke testsLive end-to-end control (Taurine): structure_lookup → PubChem CID 1123 / C2H7NO3S;
bridge_xref InChIKey → KEGG C00245, HMDB, ChEBI; gem_crosswalk → Mouse-GEM MAM.
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