cheminformatics_mcp_headless

A cheminformatics MCP server whose tools are designed, implemented, tested, and published entirely by an autonomous Claude Code loop — no human in the loop once it's set running.

What this is

Two ideas combined into one project:

1. A headless, self-driving agent loop. A single prompt (AGENT_LOOP.md) tells an agent, running non-interactively via claude -p, to do all of the following in one shot: pick a cheminformatics capability that's still missing, implement it, design a test for it, write the test, run it, iterate until it's green, and publish it — with no human approving any step. run_loop.sh drives this in a loop, one fresh process per skill, and survives Claude's usage limits by detecting the rate-limit message and backing off until it resets instead of dying.

2. An MCP server for cheminformatics. mcp_server.py dynamically discovers every skill under skills/ and exposes it as an MCP tool — dropping a valid skill folder in is the only "publish" step; nothing needs editing to add a tool.

State lives entirely on disk (progress.md, the skills/ directory) because each loop iteration is a brand-new process with no memory of the last one.

Related MCP server: ChEMBL MCP Server

Architecture

AGENT_LOOP.md  →  run_loop.sh  →  claude -p (one skill per run)
                                        │
                                        ▼
                              skills/<name>/{__init__.py, skill.py,
                                             test_skill.py, meta.json}
                                        │
                        pytest (skills/, tests/) validates it, including
                        against a shared standard-benchmark dataset
                        (tests/data/molecules.csv — see below)
                                        │
                                        ▼
                              mcp_server.py globs skills/*/meta.json
                              and registers each as an MCP tool

The skill contract

Every skill is a folder skills/<name>/ with exactly four files — see skills/README.md for the full contract:

• __init__.py — empty; makes the folder a real package.

• skill.py — a single, fully type-hinted, pure run(...) function. The MCP server introspects its signature to build the tool's JSON schema — there's no hand-written schema anywhere.

• test_skill.py — a plain pytest module (no custom test runner).

• meta.json — {"name", "description"} for the MCP tool listing.

Testing against a real, standard dataset

Hand-picked test SMILES are easy to unconsciously cherry-pick into looking right. Instead, every skill's test sweeps tests/dataset.py: a small (195-row), vendored fixture built from the ESOL/Delaney solubility set (MoleculeNet's standard small- molecule benchmark) — 175 real compounds plus 20 deliberately broken rows covering the three failure modes real-world SMILES data actually has: unparsable SMILES, NaN/empty fields, and structurally incomplete rows. See tests/data/build_dataset.py for provenance; the fixture is committed so tests never depend on network access.

This caught a real, systemic bug during development: Chem.MolFromSmiles("") returns a valid empty molecule rather than None, and Chem.MolFromSmiles(None) raises a raw TypeError — neither was caught by the obvious if mol is None: raise ValueError guard every skill had. Every skill now explicitly rejects blank/missing SMILES before it reaches RDKit.

Setup

git clone git@github.com:ASinanSaglam/cheminformatics_mcp_headless.git
cd cheminformatics_mcp_headless
python -m venv .venv && source .venv/bin/activate   # or conda/micromamba
pip install -r requirements.txt

Run the tests:

python -m pytest

Using the MCP server

The server speaks plain MCP-over-stdio, so any MCP-capable client can use it — Claude Code, a local-model harness, a raw mcp Python client, etc.

Claude Code: this repo's .mcp.json already has it configured:

{
  "mcpServers": {
    "chem_skills": {
      "command": "python3",
      "args": ["${CLAUDE_PROJECT_DIR}/mcp_server.py"]
    }
  }
}

${CLAUDE_PROJECT_DIR} is expanded by Claude Code to this repo's root, so it works regardless of where you cloned it. python3 must resolve (via PATH) to the environment you installed requirements.txt into — activate your venv/conda/micromamba environment before starting claude. (A shell function like some conda/micromamba activation wrappers won't work as the command value itself — Claude Code execs it directly rather than through your interactive shell — but an activated environment's PATH works fine, since that's inherited normally.)

Start (or restart) a Claude Code session in this repo, approve the new MCP server when prompted, then /mcp should show chem_skills connected.

Any other MCP client: point it at the same command/args as a stdio server; see mcp.client.stdio in the mcp Python SDK for a minimal example.

Running the loop yourself

./run_loop.sh 10   # builds up to 10 more skills, one claude -p process each

Progress and decisions are logged to progress.md and loop.log.

Current skills

• brics_fragmentation — Break a molecule into fragments at BRICS retrosynthetic bonds (dummy-atom labeled cut points) for fragment-library/matched-pair generation.

• canonical_tautomer — Canonicalize a molecule's tautomer using RDKit's tautomer enumeration and scoring rules, given a SMILES string.

• crippen_logp — Calculate the Crippen-method octanol/water partition coefficient (LogP) of a molecule from its SMILES string.

• double_bond_stereo — Find stereogenic C=C double bonds in a SMILES molecule and report each as E, Z, or unspecified, with summary counts.

• fraction_csp3 — Compute Fsp3, the fraction of sp3-hybridized carbons, of a molecule from its SMILES string.

• functional_group_scan — Detect common medchem functional groups (carboxylic acid, ester, amide, amines, alcohol, ether, aldehyde, ketone, nitrile, nitro, sulfonamide, halogen, aromatic ring) in a molecule via SMARTS, with match counts and atom indices.

• lipinski_ro5 — Evaluate Lipinski's Rule of Five drug-likeness (MW, LogP, H-bond donors/acceptors, violation count) for a molecule from its SMILES string.

• maximum_common_substructure — Find the maximum common substructure (MCS) shared by two molecules given their SMILES strings, returning it as a SMARTS pattern with atom/bond counts.

• molecular_formula — Compute the Hill-order molecular formula (e.g. C9H8O4) of a molecule from its SMILES string.

• molecular_weight — Compute the molecular weight (g/mol) of a molecule from its SMILES string.

• morgan_fingerprint — Compute the Morgan (ECFP-like) circular fingerprint of a molecule as a sparse on-bit list, for similarity search, clustering, or ML feature vectors.

• murcko_scaffold — Extract the Murcko scaffold (ring systems and linkers, side chains stripped) from a molecule's SMILES, optionally as a generic topology-only scaffold.

• pains_filter — Screen a molecule (SMILES) against RDKit's built-in PAINS structural-alert catalog to flag known assay-interference substructures.

• qed_score — Compute the QED (Quantitative Estimate of Drug-likeness) score and its eight constituent properties for a molecule given as SMILES.

• ring_system_analysis — Analyze ring topology of a molecule from SMILES: ring count, sizes, aromaticity, fused ring systems, and macrocycle detection.

• rotatable_bonds — Count rotatable bonds in a molecule (conformational flexibility descriptor; half of the Veber oral-bioavailability rule alongside TPSA).

• smiles_to_inchi — Convert a SMILES string to InChI, InChIKey, and canonical SMILES.

• smiles_to_molblock — Convert a SMILES string to a 2D-coordinate MDL molblock (V2000 molfile).

• standardize_molecule — Standardize a molecule from SMILES by stripping counterions/salts (largest-fragment selection), neutralizing formal charges where possible, and returning the canonical SMILES.

• stereocenter_analysis — Find tetrahedral stereocenters in a SMILES molecule and report each as R, S, or unassigned (?), with summary counts.

• substructure_match — Match an arbitrary caller-supplied SMARTS substructure query against a SMILES molecule, returning every matching set of atom indices.

• tanimoto_similarity — Compute the Tanimoto similarity between two molecules' Morgan (ECFP-like) fingerprints, given their SMILES strings.

• tpsa_descriptor — Compute the topological polar surface area (TPSA, in Ų) of a molecule from its SMILES string.

This list grows as the loop runs; each skill's own meta.json is the source of truth if this drifts out of date.