Research agents with long-lived memory fail in a specific way: they assert recorded values whose evidence no longer holds — the file drifted, the claim was withdrawn, the source was never registered. This project makes that failure mechanically checkable, fail-closed, and then measures which parts of safety the mechanism actually provides.

The structure, in one pass

 append-only canonical ledger            derived, rebuildable RAG
 ─────────────────────────────           ─────────────────────────────
 data/events/daily/*/events.jsonl   →    SQLite FTS5 passage+event index
 data/events/sources.jsonl               (+ optional offline semantic view
 (path → SHA-256 at registration)         built FROM the lexical index)
            │                                        │
            │ registered hash + line range           │ BM25 candidates
            ▼                                        ▼
 ┌─────────────────────────── MCP server (30 tools, stdio) ─────────────────────────┐
 │ memory_search_candidates → candidates only ("a score is not evidence")           │
 │ memory_fetch_evidence    → exact lines + integrity_status (matched/mismatched)   │
 │ memory_check_evidence_eligibility → fail-closed receipt; blocks silent omission  │
 │ research_prepare_ingest / research_commit_ingest → two-step, one-time HMAC       │
 │ governance_* (11 fixed roles; preflight only, never executes)                    │
 └──────────────────────────────────────────────────────────────────────────────────┘

• The ledger is the only authority; every index is a derived view that refuses to build over a drifted registered source.
• Retrieval is physically read-only (sqlite mode=ro, query_only=ON).
• Writes need a pre-existing human approval record; model-side ingestion additionally needs a one-time HMAC receipt issued outside the MCP.
• The eligibility gate verifies the integrity of cited evidence and — as of this release — fails closed when a material claim silently omits evidence the session fetched and saw fail integrity.

Every one of those sentences is backed by a specific file and line: docs/architecture-deep-dive.md walks the goal, the ledger, the RAG construction, the RAG↔MCP chain, and each control mechanism with code citations, including the boundaries that are deliberately not enforced and say so in their docstrings.

Benchmarks — what was measured, how, and what each result licenses

Every study below was preregistered before its runs (protocol and analysis code committed first; deviations disclosed in the protocol before the affected runs), scored by an independent deterministic scorer cross-checked against a condition-blinded LLM judge (judge validity: two independent raters agreed with each other κ = 1.000 and with the judge κ = 0.865 on a 50-verdict blind sample), and reported as aggregates only. Statistical choices (Wilson/Newcombe CIs, mid-p McNemar, rule-of-three) are bound to hash-verified verbatim quotes from their source papers in the citation manifests.

1 · Does the gate stop unsafe assertions? — yes, to zero, when invoked

Setup. Two paired executions, one synthetic and one real. Synthetic: 24 tasks × 2 arms × 3 reps (144 runs), each task planting one correct and one altered value in a corpus with an injected integrity fault (post-index mutation, line drift, stale index, withdrawn/missing/unregistered evidence, conflicts, plus negative controls). Real: 27 tasks over an actual eight-month research project's ledger — every fault occurred naturally; nothing was mutated for the benchmark. Paired design so each task is its own control; the model (gpt-5.6-sol, medium) and prompts are identical across arms except evidence access. Why this setup. Planted values make scoring deterministic (no judge discretion on the primary endpoint); natural faults answer the "synthetic faults are strawmen" objection. Result. Hash-detectable fault stratum: filesystem 21–22/45 unsafe vs gated 0/45 (RD 0.49 [0.33, 0.63]); real corpus 23–26/27 vs 0/27 (RD 0.85 [0.64, 0.94]); clean coverage 21/21 in both arms. What this licenses. When the eligibility workflow runs, unsafe assertions on integrity-broken evidence go to zero at no clean-coverage cost. It does not license "the MCP makes agents safe" — see benchmark 4.

2 · What does it cost? — retrieval effort, not blocking

Setup. Same paired runs, secondary endpoints: false blocks on answerable tasks, uncached tokens, evidence-binding validity. Result. False blocks 9/54 on the real corpus — all nine traced to legacy events recorded without source references (evidence-chain quality, not the gate, is the binding constraint). Tokens ~1.4–1.9× filesystem. Evidence binding valid 81/81 in the gate arm vs 52/81 filesystem. What this licenses. The gate's cost is retrieval chattiness and legacy-chain gaps, not wrongful blocking of intact evidence.

3 · Where does the payload go? — search, not verification

Setup. Byte-level decomposition of all tool results in the real-corpus gate arm (81 runs, 29.4 MB), plus a same-day optimization pass re-run. Result. 93.4% of payload is candidate search; verification itself (fetch + eligibility) is 5.3%. The optimization pass halved transport (−49% payload) with fault-unsafe still 0. What this licenses. Verification is cheap; retrieval dominates cost and is where optimization belongs.

4 · Does anyone actually call the gate? — no, not without policy

Setup. The scaffold-removal ablation (preregistered, 3 arms × 24 × 3 = 216 runs): identical tasks with no claim types, no scope preamble, no tool naming in any prompt — the original scaffold is the treatment being tested. Arms: filesystem, MCP-attached-but-unprompted, and a cheap baseline (one instruction + a registration-time hash manifest). Why this setup. Benchmark 1's 0/45 was measured under an operator prompt that told the model to use the workflow. A reviewer's question — "if the gate isn't called, there is no protection" — required measuring activation itself, per the tool-usage-awareness literature. Result. The natural arm made zero MCP calls in 72/72 runs (all 30 tools verifiably exposed): unsafe 24/45, identical to filesystem (RD exactly 0.000). The manifest baseline fixed only hash-visible faults (10/45) and misses everything semantic. What this licenses. Effective protection = activation × enforcement, and un-prompted activation is 0%. Any headline safety claim for an agent memory tool must be conditioned on activation. This is the paper's central honest finding, not a defect disclosure.

5 · Can deployable artifacts recover activation? — policy yes, schema no

Setup. Preregistered amendment, 216 more runs: a repository AGENTS.md policy file (workflow mandate + session-scope preapproval), a product-only lever (activation triggers in the two tool descriptions), and a clean rerun of the natural arm after a disclosed fixture-contamination. Result. Tool-description triggers: 0/45 activation — a dead lever. AGENTS.md: activation 42/45 (93%), unsafe halved to 12/45 — but all 12 held eligible receipts: in 9 the model fetched the faulted source, saw the mismatch, silently dropped it, and cited only intact evidence. What this licenses. A one-file repository policy restores adoption; tool schemas alone do not. And a third protection layer exists — citation discipline — because the gate can only judge the citation set it is given.

6 · Enforcing citation discipline — information loses, enforcement wins

Setup. Two more preregistered steps (72 runs each). v1.2: the server logs the session's fetches and the receipt discloses fetched-but-uncited mismatched evidence with an instruction to abstain or address it. v1.3: same detection, but an active material claim fails closed (OMITTED-MISMATCHED-EVIDENCE); citing the mismatched reference lifts the block. Result. Disclosure fired with perfect precision (13/45 fault, 0/21 clean, 0/6 negative-control) and was overridden in 9/13 — falsified by its own preregistered rule. Enforcement: unsafe 4/45 (bar ≤ 4 met), 0/12 unsafe where the block fired, zero false blocks, clean 21/21. The residual four are intact-hash semantic states (withdrawn, irrelevant) the integrity gate is documented not to judge. What this licenses. The measured ordering — information < instruction < enforcement — held at every layer tested. The enforcement ships in this package and was verified live on the installed build. Adversarial audit of the governance/ingest surface: 25/25 hostile inputs fail closed (audit).

What none of this licenses

Generalization beyond one model family and one real corpus; defense against faults whose hashes are intact (withdrawn, stale-but-valid, irrelevant evidence — measured to defeat every arm); anything about corruption that precedes registration (both arms lose 6/6 by design); agent behavior under the multi-agent governance contracts (the controls fail closed under direct adversarial input, but no model-in-the-loop governance benchmark exists yet).

Getting started — two commands to a verified research memory

pip install universal-research-mcp        # or: uv tool install universal-research-mcp
universal-research quickstart ~/my-research --yes

quickstart takes a folder of Markdown documents and does the whole RAG setup in one pass: initializes the store, registers every document's SHA-256, appends an operator-approved observation per document (the --yes is your human approval — without it, quickstart only prints a dry run), and builds the search index. Re-running it only picks up new files. No JSON authoring, no manual approval plumbing. No-install alternative, verified against the published package: uvx --from universal-research-mcp universal-research quickstart … --yes.

Connect your MCP host

The server is plain stdio — any MCP host launches the same command.

Claude Code

claude mcp add universal-research -- universal-research serve --root ~/my-research --no-auto-index

Claude Desktop — one-click: download universal-research-memory-0.9.3.mcpb and double-click (or Settings → Extensions → Install from file). Claude Desktop asks for your research folder and launches the server via uv; an empty folder is initialized automatically. Manual config alternative:

Claude Desktop / Cursor (claude_desktop_config.json / mcp.json)

{
  "mcpServers": {
    "universal-research": {
      "command": "universal-research",
      "args": ["serve", "--root", "/home/you/my-research", "--no-auto-index"]
    }
  }
}

Codex (~/.codex/config.toml, or install the plugin from plugin/universal-research-memory/)

[mcp_servers.universal_research]
command = "universal-research"
args = ["serve", "--root", "/home/you/my-research", "--no-auto-index"]

The evidence loop your agent should run

Executed verbatim against a quickstart-built store before this section was written:

1. memory_search_candidates {query: "dead-time correction", mode: "lexical"} → returns the observation as a candidate (candidate_only: true — a score is never evidence).
2. memory_fetch_evidence {path, start_line, end_line, event_id, expected_sha256} → integrity_status: "matched" and the exact cited lines; a drifted file instead returns mismatched and withholds content.
3. memory_check_evidence_eligibility {claim, claim_type, materiality, evidence:[…]} → status: "eligible" for an intact single-source result claim, and blocked: OMITTED-MISMATCHED-EVIDENCE if the session fetched a mismatched source and silently dropped it.

Adoption note (measured, not advice). In our ablation the model never called these tools without workspace policy. Add an AGENTS.md to the project that mandates the loop above and preapproves the session scope for non-interactive runs — that single file took gate activation from 0% to 93% (details).

Citation

Use the concept DOI 10.5281/zenodo.22118223 to cite the software across releases; it always resolves to the latest archived release, and each GitHub Release mints its own version DOI under it.

Development reference

python -m pip install ".[test]"
python -m pytest -q
ruff check universal_research_mcp
mypy --no-incremental --cache-dir=/dev/null universal_research_mcp
python -m build
python scripts/validate_distribution_artifact.py dist/*.whl
python scripts/ci_smoke.py dist/*.whl

License: MIT

mcp-name: io.github.mp-juns/universal-research-mcp