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kbdb MCP server

by diko316·io.github.diko316/kbdb·v1.0.3

A searchable second brain for AI agents: ranked keyword and semantic search over your Markdown.

A92/100grade A
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A92/100

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0 stars75 downloads/wk

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If you have run it, two minutes of your experience saves the next person an afternoon.

kbdb tools (19, 2 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • auto-capture-review

    Propose knowledge for storage and let the user confirm it, when the host supports elicitation.

  • check

    Verify the integrity of the knowledge base. Reports checksum mismatches, orphan sections, broken references, and circular reference chains.

  • consolidate

    Propose groups of stored sections that could become one, so a knowledge base of twenty overlapping notes can become a few good ones. Proposes ONLY -- nothing is merged. Each group reports its weakest link. Without an entailment judge kbdb CANNOT tell whether a group disagrees with itself; read every section before merging it.

  • content

    Compose a rendered Markdown document from one or more kbid or docid identifiers.

  • contradictions

    Report pairs of stored sections covering the same ground, to read together before trusting either. Similarity finds pairs; it cannot judge agreement. Without an entailment model every pair reads "related", and no "contradicts" means nothing was asked, not that nothing conflicts.

  • export

    Export the knowledge base as a self-contained directory snapshot (sections, documents, catalog -- no indexes).

  • gaps

    Report what this knowledge base is repeatedly asked and answers badly, worst first. Call it to decide what to learn next. Ranking is triage, not measurement: the log records how each search went, never whether the answer helped.

  • gcwrite action

    Remove unreferenced sections from the knowledge base. A section is eligible when no document manifest references it.

  • history

    List every revision of a section, oldest first, and name the current one. Call it when a kb-id you hold no longer appears in search: revisions are retired, not deleted, so a stale kb-id still leads to the text that replaced it. A section revised once has two revisions; one never revised has one.

  • learn

    Store durable knowledge in the agent's second brain.

  • mark_result

    Record whether a recalled section answered a query. Accumulates real usage into an eval dataset. Call it once you know whether a result helped, including when it did not -- a wrong answer is the more useful judgement.

  • neighbourhood

    List what relates to a section and HOW, so you can decide what to read next. Eight relations: supersedes, superseded-by, parent, child, sibling, references, referenced-by, similar. Seven are recorded facts; only "similar" is inferred from a cosine. Related is not relevant -- judge these against your task.

  • rebuild

    Reconstruct all index files from section files and document manifests on disk. Use this to recover from index corruption.

  • recall

    Expand remembered context after search. Retrieves full content and context for sections by identifier, returning section text, level, parent, metadata, references, and back-references. Call it on the kbids search returned. Use maxTokens to bound the payload, and format: compact when you only need the content.

  • retrieve

    Low-level fetch of a section or document by identifier. For AI agent workflows, prefer `recall` which includes metadata, context, and back-references.

  • save_evals

    Validate and persist retrieval eval cases drafted via the eval-generate prompt. Rejects cases whose kbids do not resolve, and drops duplicates. Call it only with kbids a search actually returned; invented ids fail the whole batch.

  • search

    Recall relevant knowledge by ranked keyword and semantic (hybrid) relevance. Returns ranked results with heading, snippet, matched terms, level, parent, and sectionType. Sections from the same document receive a sibling score boost when multiple sections match. Use multiple short keyword queries for best coverage. Use recall for full content.

  • status

    Get knowledge base status including document count, section count, index size, and cache statistics.

  • unlearnwrite action

    Remove a section from the knowledge base by its kb-id, or an entire document (and its member sections) by docid.

Public scan report

scanner v0.1.9 · 2026-09-21 · same rubric, same numbers if you re-run it

no findings
  • Code scan714 source files scanned25/25
  • Live reliabilityno gateway calls yet and no remote to proben/a
  • Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 20 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 92/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

What the publisher says

From the kbdb repository's README, as published. We do not edit it. Read it on GitHub

@dikolab/kbdb

A file-based knowledge base with ranked keyword and semantic (hybrid) search -- learn your documents, then recall the relevant knowledge. No external server. Runs as a CLI and MCP server.

📖 Documentation · MCP Setup · CLI Reference

GitLab

Runs on Node.js 20+ or Deno 2.6+. No database

server, no cloud account -- just files on disk.

What is kbdb?

kbdb gives AI agents a persistent, searchable second brain. Point it at your Markdown docs and it indexes them into a file-based knowledge base -- then agents (and you) recall the most relevant knowledge by ranked keyword and semantic search, not exact-key lookup. It is a living store: agents learn new facts, update them, and recall them across sessions.

No external server to install, no cloud account -- just files on disk. It runs anywhere Node.js or Deno runs, and works as an MCP server, so agents like Claude can plug it in as a memory tool.

How search works: kbdb uses keyword search by default -- synonyms are expanded, terms are ranked by relevance, and headings carry 2× weight in scoring. When an exact query finds nothing, kbdb automatically loosens the match so you still get the best available results.

Want smarter results? Use --algo hybrid to blend keyword matching with similarity search -- finding results even when different words describe the same concept. The default TF-IDF embedding provider works offline with zero setup. Swap it for a third-party provider (local ONNX model or remote API) in worker.toml when you need richer embeddings.

Knowledge stays fresh: Re-learn a file and kbdb replaces the old version automatically. Near-duplicate detection warns you when you are learning something you already have -- by embedding similarity, so it catches the same fact reworded, not just the same bytes. kbdb contradictions reports sections that cover the same ground so you can read them together. Integrity checks verify checksums, orphans and references. Confidence scores help agents tell strong matches from weak ones.

Getting Started

What You Need

One of these (pick whichever you already have):

Download

  • Node.js version 20 or newer --

Download (2.6 is the floor: the storage engine loads its WebAssembly through source-phase imports, which is what lets it run offline after one deno install. Older Deno fails with a misleading Module not found naming a .wasm file that is present.)

  • Deno version 2.6 or newer --

That's it. No database server. No extra tools.

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Install directly

Runs npx -y @dikolab/kbdb on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add kbdb -- npx -y @dikolab/kbdb
Add to Cursor

kbdb: common questions

Is kbdb MCP server safe?
Yes, by our scan: it is graded A (92/100). Read the kbdb safety report
How do I install kbdb?
It runs on your machine. Copy the Claude Code, Claude Desktop or Cursor config from the install section.
Does kbdb need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is kbdb maintained?
The last commit was 20 days ago (2026-09-01). The latest release is v1.0.3.
What can I use instead of kbdb?
Servers from other publishers that do the same job: Code Context MCP server, openchronicle-mcp server and x402 Bazaar Rank MCP server. Compare all kbdb alternatives.

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