bulla 0.35.0

Witness kernel for agent tool compositions — diagnose, attest, seal

bulla

Witness kernel for agent tool compositions — diagnose, attest, seal.

When AI agents compose tools into pipelines, implicit semantic assumptions (date formats, unit scales, path conventions) can silently produce wrong results. Schema validation passes, but the pipeline is broken. Bulla computes the coherence fee: the exact number of independent semantic dimensions that bilateral verification cannot detect.

Zero heavy dependencies. Only requires PyYAML. No numpy, no scipy, no LLM calls. Installs in under a second.

Naming: Bulla is the protocol and tool. SEAM is the underlying theory (paper).

Try it now

pip install bulla

# Audit your Cursor / Claude Desktop MCP setup
bulla audit

# Explicit config path
bulla audit ~/.cursor/mcp.json

# CI gate: fail if any composition exceeds fee threshold
bulla audit --max-fee 3 --format json

bulla audit auto-detects your MCP configuration, scans all servers, and reports cross-server coherence risks — including the boundary fee (convention conflicts that no individual server can detect on its own).

The seam problem

Two MCP servers. One uses absolute paths (/tmp/src/main.py), the other uses repository-relative paths (src/main.py). Schema validation passes. The agent silently puts the file in the wrong place. Bulla catches this before execution.

See the canonical demo →

Calibration results

Tested across 10 real MCP servers (filesystem, github, notion, playwright, tavily, etc.) in 45 pairwise compositions:

Zone	Fee	P(mismatch)	Compositions
Safe	0	0%	15/15 clean
Uncertain	1–3	0–33%	12 compositions
Unsafe	4+	~100%	18/18 confirmed

fee=0 guarantees no convention mismatch. fee≥4 guarantees real mismatches exist. The fee is computed from schemas alone — no execution required.

See calibration data for the full report.

Python SDK

from bulla import compose_multi

result = compose_multi({
    "filesystem": fs_tools,
    "github": gh_tools,
})

print(result.diagnostic.coherence_fee)   # 30
print(result.receipt.disposition.value)  # "refuse"
print(result.decomposition.boundary_fee) # 1

compose_multi() returns a ComposeResult with the diagnostic, a tamper-evident WitnessReceipt, and a fee decomposition partitioned by server. For single-server diagnosis, use compose().

Architecture

Three layers, cleanly separated:

Layer	Concern	Module
Measurement	Composition → Diagnostic (fee, blind spots, bridges)	diagnostic.py
Binding	Diagnostic → WitnessReceipt (content-addressable, tamper-evident)	witness.py
Judgment	Policy → Disposition (proceed / refuse / bridge)	witness.py

The measurement layer has zero imports from the witness layer. Measurement does not know it is being witnessed.

Commands

Command	Purpose
bulla audit	Scan MCP config, diagnose cross-server coherence
bulla gauge	Diagnose a single MCP server or manifest
bulla diagnose	Full diagnostic from a composition YAML
bulla check	CI gate with configurable thresholds
bulla scan	Scan live MCP servers (zero config)
bulla witness	Diagnose and emit WitnessReceipt as JSON
bulla bridge	Auto-bridge and emit patched YAML
bulla serve	MCP stdio server
bulla discover	LLM-powered convention dimension discovery

Output formats: --format text (default), --format json, --format sarif.

Witness-geometry diagnostics (new in 0.35.0)

Beyond the scalar coherence fee, Bulla can surface the full witness geometry of a composition: per-field leverage scores, concentration index (N_eff), coloops/loops, and the matroid-greedy minimum-cost disclosure basis. All quantities are exact rationals (Fraction), never floats.

# Show leverage, N_eff, coloops, and greedy basis on a composition
bulla diagnose composition.yaml --witness

# On a live MCP server or manifest (gauge is the prescriptive command)
bulla gauge tools.json --leverage

# Ask which hidden fields substitute for a target (effective resistance)
bulla gauge tools.json --substitutes read_file path

# Cost-weighted greedy: YAML maps "<tool>:<field>" → rational cost string
bulla gauge tools.json --costs costs.yaml

JSON output adds a witness_geometry block only when the flag is set — default output remains byte-identical to 0.34.0. The mathematical backing is described in the Witness Gram paper (14 Lean-verified theorems, 0 sorry across the program).

Quick start with bulla gauge

# Diagnose a live MCP server
bulla gauge --mcp-server "python -m my_mcp_server"

# Diagnose from a manifest JSON (tools/list response)
bulla gauge tools.json

# Save the inferred composition for hand-editing
bulla gauge tools.json -o composition.yaml

# CI gating: fail if coherence fee exceeds threshold
bulla gauge tools.json --max-fee 0

Python API

from bulla import (
    BullaGuard, WitnessBasis, PolicyProfile,
    diagnose, load_composition, witness,
    verify_receipt_consistency, verify_receipt_integrity,
)

# Load and diagnose
comp = load_composition(path="pipeline.yaml")
diag = diagnose(comp)
print(f"Fee: {diag.coherence_fee}, Blind spots: {len(diag.blind_spots)}")

# Witness with provenance
basis = WitnessBasis(declared=3, inferred=1, unknown=0)
policy = PolicyProfile(name="strict", max_unknown=2)
receipt = witness(diag, comp, witness_basis=basis, policy_profile=policy)
print(f"Disposition: {receipt.disposition.value}")

# Verify
ok, violations = verify_receipt_consistency(receipt, comp, diag)
assert verify_receipt_integrity(receipt.to_dict())

BullaGuard (high-level)

guard = BullaGuard.from_mcp_server("python my_server.py")
guard.check(max_blind_spots=0)  # raises BullaCheckError on failure

guard = BullaGuard.from_tools({
    "parser": {"fields": ["amount", "currency"], "conventions": {"amount_unit": "dollars"}},
    "engine": {"fields": ["amount"], "conventions": {"amount_unit": "cents"}},
}, edges=[("parser", "engine")])

MCP Server

Bulla exposes a JSON-RPC 2.0 stdio server with two tools and one resource:

bulla serve   # starts MCP stdio server

• bulla.witness — composition YAML → WitnessReceipt (structured output)
• bulla.bridge — composition YAML → patched YAML + receipt chain
• bulla://taxonomy — convention pack taxonomy

Convention Packs

Layered vocabulary for convention recognition. Later packs override earlier ones.

bulla diagnose --pack financial.yaml pipeline.yaml
bulla scan --pack custom.yaml "python server.py"

Ships with base (11 dimensions) and financial (4 domain-specific dimensions).

CI Integration

# GitHub Actions with SARIF
- run: pip install bulla
- run: bulla check --format sarif compositions/ > bulla.sarif
- uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: bulla.sarif

How it works

Bulla builds a coboundary operator from tool dimensions to edge dimensions for both the observable and full sheaves. The coherence fee is:

fee = rank(δ_full) − rank(δ_obs)

Each unit of fee is an independent semantic dimension invisible to pairwise checks. Bridging increases rank(δ_obs) until it matches rank(δ_full). Rank computation uses exact arithmetic (fractions.Fraction) — no floating-point, no numpy.

Witness Contract

Every receipt binds three hashes: composition (what was proposed), diagnostic (what was measured), receipt (what was witnessed). Receipts chain via parent_receipt_hash for auditable repair flows.

See WITNESS-CONTRACT.md for the normative specification.

License

Business Source License 1.1

Use grant: non-competing use, plus commercial use processing fewer than 1,000 compositions per month. Converts to Apache 2.0 on 2030-04-01.