world-model-mcp
This server provides persistent, queryable memory plus a signed, tamper-evident audit trail for AI coding agents, exposed as MCP tools.
Persistent knowledge graph: store and query facts about code (APIs, functions, classes, files, packages) with provenance, confidence, and entity/type filters.
Learn from corrections and decisions: record user corrections, approvals/rejections, and decision traces; surface them on future relevant queries.
Validate changes: check proposed edits/creates/deletes against learned constraints (linting, architecture, testing, API contracts, style) before they land.
Regression and bug awareness: fetch bugs tied to files, predict regression risk, simulate blast radius, and predict likely test failures.
Project onboarding: seed the knowledge graph from an existing codebase and ingest GitHub PR review comments as constraints.
Adversarial verification: independently verify answers against grounded facts using a Coach LLM, with HIGH/MEDIUM/LOW confidence and claim-level source pointers.
Contradiction management: find and resolve conflicting facts using confidence-weighted strategies (auto, recency, source count, manual).
Context and compaction support: retrieve pre-action context bundles, re-inject constraints/facts after context compaction, and audit what was remembered across compaction boundaries.
Signed audit chain (opt-in): enable tamper-evident logging with hybrid Ed25519 + SLH-DSA signatures, prove entry inclusion, and get the audit log head for compliance checks.
Human annotations: pin signed human notes, overrides, or intervention records to event spans in the same audit chain.
Health and diagnostics: get memory health reports (orphans, stale facts, contradictions, decay candidates, DB sizes) and search across all registered projects.
Artifact generation and transcript recall: export a CLAUDE.md summary from the knowledge graph, parse AGENTS.md/CLAUDE.md constraints, and hydrate session transcripts by line range.
Integrates with ESLint to provide linting validation for JavaScript/TypeScript code, detecting violations and suggesting fixes.
Integrates with GitHub to ingest pull request review comments and convert team feedback into constraints for future code editing.
Integrates with Ruff to provide linting validation for Python code, detecting violations and suggesting fixes.
World Model MCP
Persistent memory + optional signed audit for AI coding agents.
world-model-mcp is persistent memory plus a post-quantum-signed, offline-verifiable audit trail for AI coding agents (FIPS 205 hybrid Ed25519 + SLH-DSA), MIT-licensed and fully local.
world-model-mcp ships a local SQLite knowledge graph your agent queries every turn: hallucinations become verifiable, corrections stick across sessions, and regressions get caught before they land. Flip on the audit chain and every event is signed with FIPS 205 hybrid Ed25519 + SLH-DSA, verifiable offline forever. MIT-licensed, runs entirely local, works with 10+ AI coding agents including Claude Code, Cursor, Codex, Continue, Cline, Windsurf, GitHub Copilot Chat, pi, OpenClaw, and Hermes Agent.
Latest: v0.16.3. Metadata refresh. No code changes. The PyPI summary and keyword set now name both artifacts that ship in this wheel: the MCP world-model memory server for coding agents, and the
etch-verifyoffline audit-chain verifier CLI. Both were already installed by every prior release; the metadata is now honest about that so a searcher looking for an offline audit-chain verifier can find the package. v0.16.2 shipped CI hardening plus theworld-model demolive notary beat introduced in v0.16.0 (three sample decisions signed into a real hybrid-signed epoch, verified VALID, mutated one byte to prove tamper detection fires live, then restored). Full version history in CHANGELOG.md.
Try it in 10 seconds
pip install -U world-model-mcp && world-model demoYou will see three sample decisions signed into a tamper-evident epoch, then the demo mutates one byte and re-verifies to prove the tamper is detected instantly (INVALID), then restores and re-verifies (VALID again). Offline, no account, no network. A world-model-demo-receipt.json lands in your current directory and a shareable etch.systems/verify#… URL is printed for anyone to check the same receipt in a browser.
mcp-name: io.github.SaravananJaichandar/world-model-mcp
Related MCP server: context-atlas
Prefer a managed hosted version?
If you want the same signed audit chain without running the OSS server yourself, etch.systems ships a managed variant that uses this package as its offline reference verifier.
No infrastructure to run (POST your first event in 30 seconds, no signup)
Compliance framework mapping to SR 11-7, EU AI Act Article 12, ISO 42001, NIST AI RMF, SOC 2 CC7
External anchoring shipped end-to-end (Sigstore Rekor + Bitcoin OpenTimestamps)
Portable per-agent identity with key rotation and delegation across MCP client tools (Claude Code, Cursor, Continue, Cline, Codex)
Offline verifier stays the same:
pip install world-model-mcp && etch-verify manifest.jsonverifies the hosted chain against pinned public keys without depending on the hosted service being online
curl -X POST https://etch.systems/v1/your-projectReturns a bearer token and an MCP endpoint you can point any MCP-compatible client at. Full docs at etch.systems.
FAQ
What is world-model-mcp? world-model-mcp is persistent memory plus a post-quantum-signed, offline-verifiable audit trail for AI coding agents, exposed as an MCP server the agent queries every turn. It runs entirely local, ships as MIT-licensed Python + optional adapters for 10+ AI coding agents.
How is it different from Mem0, Letta, and other agent-memory tools?
world-model-mcp is the only agent-memory tool that ships a hybrid post-quantum signed audit chain (FIPS 205 Ed25519 + SLH-DSA-SHA2-128f) alongside memory, with an offline reference verifier (etch-verify) and dual external anchoring (Sigstore Rekor + Bitcoin OpenTimestamps) available via the hosted etch.systems companion. Peer tools (Mem0, Letta, agentmemory) ship memory without a signed audit chain. See the "Compared to" table below for a per-mechanism breakdown.
Is the audit trail post-quantum secure? Yes. Every closed epoch carries a hybrid signature: Ed25519 (classical) plus SLH-DSA-SHA2-128f (FIPS 205 stateless hash-based post-quantum signature). A future quantum adversary that breaks Ed25519 alone still faces the SLH-DSA signature over the same payload; both signatures must forge for the chain to be forged.
How do I verify a record offline, with no account?
Run pip install -U world-model-mcp && world-model demo. The demo signs three sample decisions into a real hybrid-signed epoch, exports the same manifest format the etch-verify CLI reads, verifies VALID, mutates one byte to prove tamper detection is live (INVALID), then restores and re-verifies. A world-model-demo-receipt.json lands in your current directory and a etch.systems/verify#… URL is printed for anyone to check the receipt in a browser. Zero signup, zero network.
Which AI coding agents does it work with?
Claude Code, Cursor, Codex, Continue, Cline, Windsurf, GitHub Copilot Chat, pi, OpenClaw, and Hermes Agent. Each has a dedicated starter repo under github.com/SaravananJaichandar/world-model-mcp-<agent>-starter. The MCP wire format is standard, so any MCP-capable client can consume the same tools.
Should I use this OSS package or the hosted etch.systems service?
Use this OSS package (MIT-licensed, fully local) if you want to run the audit chain on your own infrastructure or add signed memory to an internal tool. Use etch.systems if you want the same audit chain shipped as a managed service with compliance framework mapping (SR 11-7, EU AI Act Article 12, ISO 42001, NIST AI RMF, SOC 2 CC7) and portable per-agent identity across MCP client tools. The offline reference verifier is the same in both cases: pip install world-model-mcp && etch-verify manifest.json verifies either an OSS-generated or a hosted-generated manifest against pinned public keys.
Compared to other agent-memory + agent-audit projects
Head-to-head positioning against eight named peers on the audit / signing / anchoring dimensions the agent-memory space is converging on. Every world-model-mcp cell carries a provenance flag: own = measured / observed in the shipped product; cited = pulled from the competitor's own public landing, repo, or press.
Source of truth for this table lives in the hosted service repo: world-model-mcp-hosted/src/etch/competitor_matrix.py. Update in both places if you change either.
world-model-mcp (this repo) + Etch (hosted) | Mem0 | Letta | agentmemory | Unicity AOS | Repowise | Trinitite | Caura | FailproofAI | |
Signature scheme | Ed25519 + SLH-DSA-SHA2-128f (hybrid) | None (encryption at rest only) | None | None | BLAKE3 hash-chain | Deterministic signals (no signing) | Signed + hash-chained (algo not disclosed) | None | None disclosed |
Post-quantum ready | Yes (SLH-DSA, NIST FIPS 205) | No | No | No | No (BLAKE3 hash only) | No | No disclosed | No | No |
Offline reference verifier | etch-verify CLI, streaming, ships with PyPI package | No | No | No | Not disclosed | No (SaaS-only) | Browser-based verifier | No | No |
Attestation cadence enforcement | Scheduled systemd timer + on-demand verify | No | No | No | Not disclosed | No | Scheduled attestation runs | No | No |
Framework mapping (Article / control IDs) | EU AI Act Art. 12-15, SOC 2 CC6.6/7.2/7.3, ISO 27001 A.12.4/A.14.2 | SOC 2 + HIPAA (badges, no per-control mapping) | Not disclosed | No | Not disclosed | EU AI Act (negative-space claim) | Per-Article citations (EU AI Act, SOC 2, SR 11-7) | SOC 2 in-progress badge | SOC 2 enterprise tier only |
Drift detection | Per-epoch chain integrity + attestation trend | No | Success-rate + error tracking | No | Not disclosed | Code-health score deltas | Deterministic replay + risk-score deltas | No | Evaluator score deltas |
Annotation support (signed human notes in-chain) | pin_annotation MCP tool, signed into same chain | No | No | No | Not disclosed | No | Not disclosed | No | No |
Browser verifier | Yes, chain-integrity widget on /auditor/<slug> | No | No | No | Not disclosed | No | Yes | No | No |
Share link (auditor access, no login) | Yes, expiring share tokens (bs_ prefix, 30d max) | No | No | No | Not disclosed | No | Yes, expiring auditor access | No | No |
External anchor (independent witness log) | Dual: Sigstore Rekor + Bitcoin OpenTimestamps (public, opt-out per project) | No | No | No | Internal hash-chain only (no external log) | No | Not disclosed | No | No |
OSS license | MIT (world-model-mcp) | Apache 2.0 | Apache 2.0 | Apache 2.0 | Not disclosed | AGPL v3 (core) | No OSS | Apache 2.0 | No OSS |
GitHub stars | Snapshot via etch.systems/api/oss-stats | 61.6k | 23.9k | 24.9k | 7.1k | 4.2k | No public repo | 373 | No public repo |
Public funding | Bootstrapped | $24.5M | $10M | Not disclosed | $3M seed Feb 2026 | Not disclosed | Not disclosed | Not disclosed | Not disclosed |
Compliance posture (as claimed on landing) | SOC 2 Type I in progress (Aug 2026 target) | SOC 2 + HIPAA (badges on trust subdomain) | Not disclosed | No compliance posture | No compliance posture | No SOC 2; EU AI Act negative-space claim | Per-Article compliance framing | SOC 2 in-progress badge | SOC 2 enterprise-tier |
How to read this table:
Bold cells describe mechanisms shipped in this repo (OSS) or the hosted service (etch.systems).
Peer cells are cited from each competitor's public landing / repo / press. Not our own measurement.
[own]on aworld-model-mcpcell means we measured / observed the mechanism ourselves.[cited]means the value came from a third-party source.Only the audit / signing / anchoring dimensions are in this table. General memory features (retrieval accuracy, adapter breadth, LLM support) are covered in the Features section below.
Numbers
Benchmark | Score | Details |
+10.2 points as a single-trial upper bound (67.3% → 77.6% on 49 paired instances); the multi-seed mean effect is +0.24 per instance, 95% CI [0, 0.47] | Pre-registered, Claude Code 2.1.177 headless, Zenodo DOI 10.5281/zenodo.21076824. Within-domain +15.0 pts, cross-domain +6.9 pts with zero regressions on the single-trial split. | |
100.0% on | 105 pairs × 19 categories, deterministic (no LLM). Shipped since v0.11.0. | |
100.0% exact match | 12 hand-labeled pairs (4 grounded, 4 partial, 4 hallucinated). Layer 3 adversarial verification via independent Coach LLM. Shipped since v0.12.12. |
The SWE-bench number is the load-bearing empirical claim. The other two are internal correctness benchmarks for shipped components. Reproducibility scripts in each benchmark directory or the linked repo.
Testing
1,494 unit + integration + fuzz tests across the shipped codebase. Coverage floor gated at 71% / 66% (two-tier for CI runners with and without SLH-DSA in liboqs).
# Run tests
pytest -q
# With coverage
pytest --cov=world_model_server --cov-report=term-missing
# Fuzz targets (Atheris, requires Clang / libFuzzer)
python fuzz/fuzz_verify_manifest.py fuzz/corpus/ -max_total_time=60
# Non-Atheris smoke fuzz (runs in every CI pass, no system deps)
pytest tests/test_fuzz_smoke_verify.py -qSelected test suites worth calling out:
FIPS 205 SLH-DSA known-answer tests (
tests/test_fips_205_slh_dsa_kat.py): locks parameter sizes for SLH-DSA-SHA2-128f (public key = 32 bytes, secret key = 64 bytes, signature = 17,088 bytes), sign/verify round-trip with wrong-key + wrong-message + mutated-signature rejection, plus a fixed KAT-vector fixture (tests/fixtures/slh_dsa_kat_vectors.json) that must verify true forever.Streaming verifier byte-parity (
tests/test_etch_verify_streaming.py): locks byte-identical output between in-memory and streaming exporters so an auditor hashing the manifest as an artifact of record gets the samemanifest_sha256regardless of which exporter the operator used.Contradiction benchmark (
benchmarks/contradictions-200/): 105 pairs × 19 categories, deterministic. Locks theautostrategy at 100% on every commit.
CI gates coverage floor + full test suite on every push. See .github/workflows/pytest.yml.
Authenticated audit chain (v0.13+, opt-in)
For deployments where the audit trail must be cryptographically verifiable (SOC 2, HIPAA, EU AI Act, or your own internal control list):
export WORLD_MODEL_AUDIT_LOG=on
# then start the world-model-mcp server as normalOn the first opt-in start, the server creates two new SQLite tables in the existing audit.db file (tamper_evident_log and tamper_evident_epochs) and generates a new hybrid keypair on the first epoch close. Every subsequent event is appended to a SHA-256 Merkle chain. When an epoch closes (default: 1024 events), the chain root gets signed with a hybrid Ed25519 + SLH-DSA-SHA2-128f envelope: classical + post-quantum, so a hypothetical break of elliptic-curve cryptography leaves the audit trail intact.
Verifiable offline, forever.
etch-verifyCLI ships with the PyPI package; auditors run it on their laptop, no network access needed after the initial download.Signatures prove authorship, not just order. Hash-chain alternatives can prove nothing was reordered, but cannot prove WHO signed. This chain answers both.
No dashboard, no signup, no external service. Runs entirely in your process against local SQLite.
Full write-up in docs/AUDIT_LOG.md.
For a hosted version that adds KMS-backed keys, a public transparency log, external anchoring to Sigstore Rekor + Bitcoin OpenTimestamps, and a compliance-facing operator dashboard, see the Etch companion below.
Quick Start
Three most-common install paths. For every other supported client (Cursor, Cline, Codex, Continue, Copilot, Windsurf, Goose, pi, OpenClaw, Hermes, and more), see etch.systems/docs/install.
Option 1: Claude Desktop (one-click)
Download the latest .mcpb from Releases and drag it into Claude Desktop. Auto-installs hooks, MCP server config, and dependencies.
Option 2: Claude Code / IDE plugins (pip install)
# 1. Install the package
pip install world-model-mcp
# 2. Set up in your project (auto-seeds the knowledge graph from existing code)
cd /path/to/your/project
python -m world_model_server.cli setup
# 3. Restart Claude Code
# Done. The world model is pre-populated and active.Next (optional): turn the local signed audit log into an auditor-verifiable chain with the hosted Etch notary — KMS-backed keys, public transparency log, external anchoring, and a share link for auditors, with no infra to run. See Hosted companion: Etch.
Option 3: HTTP transport for remote / MCP-tunnel deployment
pip install 'world-model-mcp[http]'
python -m world_model_server.server --transport http --port 8000Exposes MCP over Streamable HTTP so remote agents can connect over the wire. See docs/http_transport.md for auth, CORS, and reverse-proxy setup.
Other clients
Install via the OSS CLI. Each command writes the correct config for that client (defaults to sys.executable as the interpreter path, per-client-format-aware, safe against overwrite via --force and --dry-run flags):
python -m world_model_server.cli install-cursor # Cursor
python -m world_model_server.cli install-cline # Cline
python -m world_model_server.cli install-codex # Codex
python -m world_model_server.cli install-continue # Continue (also --global)
python -m world_model_server.cli install-copilot # GitHub Copilot Chat (VS Code Insider)
python -m world_model_server.cli install-windsurf # Windsurf
python -m world_model_server.cli install-pi # pi
python -m world_model_server.cli install-openclaw # OpenClaw
python -m world_model_server.cli install-hermes # Hermes (MCP mode)
python -m world_model_server.cli install-hermes-provider # Hermes (Elixir-native provider)Full per-client walkthroughs (with verify commands + troubleshooting) at etch.systems/docs/install.
What it does
world-model-mcp is a temporal knowledge graph that sits between your AI coding agent and its work. It records facts, entities, and constraints from your codebase; validates every code change against learned constraints at the edit boundary; re-injects relevant context after context-window compaction; tracks contradictions with confidence-weighted resolution; and adversarially verifies retrievals via an independent Coach LLM.
Features
1. Hallucination prevention. Every fact recorded carries provenance (asserted_by, confirmer, confirmation_state, evidence_type). When the agent queries a fact, it gets the confidence score along with the answer. When two facts contradict, the newer/higher-confidence one wins; the loser is retained with superseded_by. The agent never has to guess whether a stored fact is still current.
2. Learning from corrections. When you correct an agent (record_correction), the correction is stored as a first-class event with the entities involved. Next time the agent queries anything touching those entities, the correction surfaces first. Corrections stick across sessions, across context compactions, across agent restarts.
3. Regression prevention. Every code-change proposal runs through validate_change, which walks the constraint graph and returns violations before the edit lands. Constraints are learned automatically from your codebase (seed_project), reinforced by PR review comments (ingest_pr_reviews), and hand-authored (record_event). The agent sees the violation, the suggestion, and the source of the constraint.
4. Coach-Player adversarial verification. A Player agent drafts a decision. An independent Coach agent independently requeries the graph for precedent, walks the Merkle proof, checks the hybrid signature, and only then approves. The Coach never trusts the Player's summary; it reverifies against a signed ledger every time. 100% exact match on 12 hand-labeled pairs.
Full technical architecture in docs/ARCHITECTURE.md.
MCP Tools
Eight tools shipped. One-line summary; click each for signature + examples in docs/mcp/.
Tool | What it does |
| Fetch a stored fact with provenance + confidence + source |
| Append an event to the audit chain ( |
| Check a code-change proposal against learned constraints, returns violations + suggestions |
| List all constraints matching an entity or file pattern |
| Store a user correction so it re-surfaces on next relevant query |
| Fetch bugs touching the given files + a per-file risk score |
| Bulk-ingest an existing codebase into the knowledge graph |
| Turn recent PR review comments into constraints |
Full tool documentation at docs/mcp/.
Hosted companion: Etch
Running world-model-mcp locally? Etch (etch.systems) is the hosted governance plane built on the same OSS core, adding what a compliance team needs to sign off on production use:
KMS-encrypted signing keys (never plaintext at rest)
Public transparency log with signed head (split-view resistance)
External anchoring to Sigstore Rekor + Bitcoin OpenTimestamps (dual independent witness)
Operator dashboard with session traces, chain integrity view, PII scanning, and a client-answer PDF export
Standalone auditor CLI + browser verifier at
/auditor/<slug>Free tier, pay-as-you-grow beyond that
Same crypto primitives, same audit-log schema, zero source changes to the shipped OSS. Skip if you're running standalone.
Which one is right for me?
Situation | Route |
I want persistent memory for my local AI coding agent |
|
I want to prove agent decisions to a regulator six months later | |
I want signed evidence bundles a court can self-authenticate | |
I want cross-team or cross-vendor federation of signed audit history | |
I want to try both | Start local with |
How it works
world-model-mcp is an MCP server (stdio or Streamable HTTP) that exposes a temporal knowledge graph backed by SQLite. Every agent turn queries the graph; every code change or correction writes back. On startup the server auto-seeds from your existing codebase; on shutdown it flushes cleanly.
Six databases under .claude/world-model/:
entities.db: files, functions, classes, symbolsfacts.db: semantic facts with provenance + confidencerelationships.db: dependencies, calls, importsconstraints.db: learned rules the agent must respectsessions.db: per-session context trackingevents.db: immutable event log (audit chain when opted in)
Full architecture with diagrams in docs/ARCHITECTURE.md.
Configuration
Environment variables (all optional):
Variable | Purpose | Default |
| Enable the signed audit chain |
|
| Override the SQLite databases directory |
|
| Opt in to anonymous OSS telemetry (see Privacy) |
|
| Only used if you enable optional LLM-backed features | none |
Full configuration reference in docs/CONFIGURATION.md.
Privacy and Security
Telemetry is off by default. If you opt in with
WORLD_MODEL_TELEMETRY=on, aggregated install-level metrics ship toetch.systems/api/telemetry/ingest(endpoint URL, no source code, no prompts, no PII). Right-to-erasure supported viaDELETE /api/telemetry/install/{install_id}.No
ANTHROPIC_API_KEYis required for core operation. Some optional features (Coach-Player Layer 3 verification, LLM-backed reranking) use the API if a key is provided. Without it, everything else works.Report a security issue: email security@etch.systems. PGP key on request.
Full write-up in docs/PRIVACY.md.
Contributing
Contributions are welcome. See CONTRIBUTING.md for development setup, coding standards, adding language support, writing tests, and submitting PRs.
Areas where help is especially wanted:
Language parsers (Go, Rust, Java, C++)
Additional MCP-client adapters
Framework integrations (LangGraph, CrewAI, AutoGen, LlamaIndex; starter shims exist in the hosted repo)
Benchmark contributions in
benchmarks/
Please read CLA.md before your first PR.
License
MIT License. Free for commercial and personal use.
Links
Full version history: CHANGELOG.md
Documentation: docs/
Hosted service: etch.systems
Benchmarks: benchmarks/ + coding-agent-memory-benchmark
Support: support@etch.systems
Zenodo (formal citation): DOI 10.5281/zenodo.20834508
Available Tools
31 toolsexport_claude_mdB
Generate a CLAUDE.md document from the knowledge graph (top constraints, recent decisions, known bug regions, co-edit patterns).
Inputs: max_constraints.
find_contradictionsC
Find pairs of facts that contradict each other based on similarity and status differences
Inputs: limit, query.
get_agents_md_constraintsA
Parse AGENTS.md / CLAUDE.md / GEMINI.md / .agents/skills/*.md in the project and return declarative constraints. Mixed into PreToolUse enforcement automatically; this tool exposes the same data for inspection.
Inputs: file_path, project_dir.
get_audit_log_headA
v0.13 tamper-evident audit log. Return the current head state (last log entry seq, last closed epoch seq, unclosed-entry count) plus the full closed-epoch chain with hybrid signature envelopes. Compliance auditors call this periodically to verify no operator misbehavior has occurred since the last check. Requires WORLD_MODEL_AUDIT_LOG=on at server startup.
Inputs: no named parameters.
get_co_edit_suggestionsB
Get files commonly edited alongside the given file based on historical patterns
Inputs: file_path (required), limit.
get_compaction_auditA
List recent compaction audit entries, most-recent first. Filter by session_id or limit count.
Inputs: limit, session_id.
get_constraintsA
Get constraints (linting rules, patterns, conventions) for a file
Inputs: file_path (required), constraint_types.
get_context_for_actionC
Pre-action context bundle: constraints, decisions, bugs, co-edits, related facts, and risk score for a file before editing
Inputs: file_path (required), action_type (required).
get_decision_logC
Get decision traces showing agent proposals and human corrections
Inputs: limit, file_path, session_id, decision_type.
get_health_reportA
Memory health diagnostics: orphans, stale facts, contradictions, decay candidates, DB sizes
Inputs: no named parameters.
get_injection_contextB
Return a compact constraint+fact bundle for PostCompact / UserPromptSubmit hooks to re-inject after context loss.
Inputs: event_type (required), max_facts, project_hint, max_constraints.
ingest_pr_reviewsA
Pull GitHub PR review comments and convert them into learned constraints in the knowledge graph
Inputs: repo, count.
pin_annotationA
Attach a signed human annotation (note, override rationale, or intervention record) to a span of agent events. Persists into the annotations table and chains into the same Merkle audit log as agent writes (v0.15.0, ADR-0001). Rationale limited to 8 KB.
Inputs: author (required), rationale (required), session_id (required), annotation_type (required), +2 more.
predict_regressionA
Score regression risk for a proposed change to a file based on past bugs, test failures, and constraint violations
Inputs: file_path (required), change_description.
predict_test_failuresA
Surface tests likely to fail given a set of edited files
Inputs: file_paths (required).
promote_constraintB
Promote a constraint from this project to all other registered projects
Inputs: constraint_id (required), target_projects.
prove_entry_inclusionA
v0.13 tamper-evident audit log. Return a cryptographic inclusion-proof bundle for a persisted row_id (fact, constraint, event, or decision ID). Bundle includes the entry, the containing signed epoch (Ed25519 + SLH-DSA hybrid signature envelope), an RFC 6962 Merkle inclusion proof, and the full epoch chain from genesis. Requires WORLD_MODEL_AUDIT_LOG=on at server startup; returns an error object when opt-in is off, when the row_id is not found, or when the entry is in the unclosed backlog.
Inputs: row_id (required).
query_factB
Query the knowledge graph for facts about entities (APIs, functions, classes, etc.)
Inputs: query (required), context, entity_type, content_type.
recall_transcript_rangeC
Hydrate a Claude Code session transcript by line range. Lets agents trace a fact back to the exact conversation that produced it.
Inputs: session_id (required), line_end, line_start.
record_compaction_auditA
Record a context-compaction event with token counts and what was re-injected. Lets developers audit what was remembered across compaction boundaries.
Inputs: session_id, raw_summary, facts_injected, injection_event, +3 more.
record_correctionC
Record a user correction to Claude's output (high-priority learning signal)
Inputs: session_id (required), claude_action (required), user_correction (required), reasoning.
record_decisionC
Record a decision trace: what the agent proposed and how the human responded
Inputs: session_id (required), decision_type (required), file_path, reasoning, +3 more.
record_eventC
Record a development event (file edit, test run, etc.)
Inputs: event_type (required), session_id (required), description (required), success, +3 more.
record_test_outcomeC
Record test results and link failures to recent code changes
Inputs: session_id (required), test_results (required).
resolve_contradictionC
Pick a winner between two contradicting facts using a confidence-weighted strategy (auto, keep_higher_confidence, keep_most_recent, keep_most_sources, supersede_a, supersede_b, manual).
Inputs: fact_a_id (required), fact_b_id (required), notes, strategy.
search_globalC
Search entities across all registered world-model projects
Inputs: query (required), limit.
seed_projectA
Scan the project codebase and populate the knowledge graph with entities and relationships from existing code
Inputs: force, project_dir.
simulate_changeC
Project blast radius and historical outcomes for a proposed change
Inputs: file_path (required), change_description (required).
validate_changeB
Validate a proposed code change against known constraints
Inputs: file_path (required), change_type (required), proposed_content (required).
verify_retrievalA
Adversarially verify an answer is grounded in a specific set of facts. An independent Coach LLM call checks each material claim in the answer against the supplied source facts and returns confidence (HIGH / MEDIUM / LOW), verified + unverified claim lists, and per-claim source_pointers. Never raises; failures return LOW + error populated. v0.12.12.
Inputs: query (required), answer (required), fact_ids (required), verification_model.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 31 tools
Several tools have poorly separated boundaries: validate_change, simulate_change, predict_regression, and predict_test_failures all appear to assess the impact of a proposed change, differing mainly in subtle emphasis. Similarly, query_fact, search_global, and get_context_for_action overlap heavily in fact retrieval, and record_correction, record_decision, and pin_annotation all capture human feedback. An agent would frequently struggle to select the right tool without reading every description.
Every tool follows a consistent snake_case verb_noun pattern, e.g., get_constraints, record_event, predict_regression, prove_entry_inclusion. Even the more unusual names like pin_annotation and seed_project fit the same imperative structure. This is a highly predictable and uniform naming convention.
31 tools is well beyond the 25+ threshold for a single server and will overwhelm tool selection, especially given the many overlapping prediction and retrieval tools. The server would be more coherent with roughly half the current surface area, consolidating related reads and writes into broader commands.
The domain is broadly covered: it supports knowledge-graph population and queries, event and decision recording, constraint ingestion and validation, regression prediction, audit-log integrity, compaction auditing, and context export. Minor gaps exist, such as no explicit fact/constraint update or delete lifecycle and no project listing tool, but the core workflows are well supported.
Maintenance
Related MCP Connectors
Shared project memory for AI coding agents: decisions, lessons, risks and tasks in one graph.
Codebase knowledge graph — architectural awareness for AI coding agents via MCP
Codebase intelligence for AI coding agents: graph, git history, docs, decisions, code health.
Change-aware CI validation and affected-test guidance for coding agents.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceProvides AI coding agents with pre-edit situational awareness by combining structural call graphs and co-change history to prevent incomplete edits. It surfaces files that historically change together, reducing missed coupled modules.3MIT
- AlicenseNot gradedqualityBmaintenanceEnables coding agents to query a local, versioned knowledge graph of a software project, retrieving overviews, context packs, evidence, and explanations to make informed changes.MIT
- AlicenseAqualityBmaintenanceEnables AI agents to maintain a persistent knowledge graph of a project, providing dependency context, impact analysis, side-effect discovery, and session recording for more informed coding decisions.5MIT
- FlicenseBqualityBmaintenanceTurns local code repositories into a queryable dependency graph built from AST parsing and git co-edit history, then exposes it alongside an Obsidian vault as persistent memory. Enables context retrieval, impact analysis of proposed changes, and curation of durable prose knowledge through full-text search and Personalized PageRank classification—all fully offline without API keys or embeddings.30-