linza-mcp 0.2.1

Local-first MCP sidecar for agent workspaces, artifacts, review intents, and context export.

LINZA - Local MCP Server for Agent Work with Knowledge Folders

It does not change your data. It changes how you see it.

LINZA works with Obsidian vaults, Markdown folders, documents, articles, logs, and drafts. It is useful when there is already too much material and you want to understand the knowledge base, identify its main areas, and teach an agent to navigate it well.

Russian version

LINZA reads a selected folder, builds a local SQLite database next to it at .linza/linza.db, and gives the agent a working map: which topics exist in the material, which formats repeat, which notes may be related, where cause/effect chains appear, and what may be useful in future sessions.

Source files remain untouched. LINZA does not rewrite notes during indexing, does not turn a raw log into a rule, and does not teach the agent behind your back. It turns hypotheses into short intents - proposed actions with evidence. The user decides, the agent executes.

doctor -> index -> map -> review intents -> teach -> grow preview -> explicit apply

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What LINZA Is For

LINZA collects several concrete things that help agents work with a knowledge base:

1. Folder map How many notes were found, whether the index is fresh, which areas are visible, and which materials are waiting for your review.

2. Areas Large semantic groups. Their names remain drafts until you accept or rename them.

3. Material formats Logs, drafts, specifications, research notes, cases, rules, and other recurring forms found in the folder.

4. Relations Possible neighborhoods, hierarchy, cause/effect, and routes between nodes. LINZA should show not only how documents are related, but also why.

5. Memory for future agents Short candidates: what to remember, when to recall it, what is stale, and what looks uncertain.

6. Context packs Compact selections for an agent: selected context with sources, relations, and boundaries.

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Material Formats

A material format is the user-facing name for a recurring note form. Examples: diagnostic log, decision, article draft, research note, specification.

LINZA first sees only structure: length, headings, lists, links, tables, folders, and recurring signals. That means the first result may have a neutral name: type-001. The user can say: "these are logs". LINZA then stores the mapping type-001 -> logs in .linza.

The internal API keeps the old compatibility keys material_type, type_name, and role. Externally, the documentation and user-facing view say "format" because that is closer to how users actually think about their material.

Important boundary:

• accepting a format name records a decision in .linza;
• writing role: logs to YAML is only possible through a separate review intent;
• note text does not change.

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What Review Looks Like

LINZA returns information roughly like this:

LINZA is ready

Material:
- 42 notes indexed
- 3 incoming artifacts waiting for review
- service database: .linza/linza.db

Next step:
1. Review discovered areas
2. Accept, rename, or skip 3-5 review intents
3. Nothing is written without a dry-run and explicit apply

Review intent:
Accept material format "diagnostic logs" from 8 examples
Why: similar structure, repeated headings, nearby chunks
What changes: the format name is stored in .linza; Markdown notes do not change

Internally, each intent remains a structure with an ID, evidence, and the data needed to preview the change and then confirm/write it. To you, LINZA returns a ready user-facing view so the agent can show a clear answer instead of JSON.

A good intent always answers the main question: why does LINZA think this? It should include sources, chunks, relation type, confidence, and an honest description of what will change after applying it.

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Teaching And Growth

The autonomy model is:

1. review_next shows review intents in a readable user-facing view.
2. The user accepts, renames, or skips.
3. apply_review_items runs dry-run first.
4. After confirmation, the selected intent is written to .linza or to compact YAML, if that write type supports it.
5. teach selects good accepted examples.
6. grow proposes similar intents from those examples and explains selected_rules, the reasons they entered the batch.

If you accepted the wrong thing, the approval can be softly revoked:

agent_workspace(action="history")
agent_workspace(action="revoke_approval", approval_id=17, dry_run=false)

LINZA does not delete the old record and does not try to automatically roll back YAML. It marks the approval as revoked, stops using it as an active example, and keeps a trace in history.

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Installation

1. Install the package

python -m pip install linza-mcp

If you need LINZA to read PDFs directly:

python -m pip install "linza-mcp[pdf]"

The regular install is already enough for Markdown, TXT, JSON, DOCX, and XLSX. [pdf] adds the local PDF extractor pypdf.

2. Choose a folder

LINZA works with any Markdown folder: an Obsidian vault, a project workspace, or a separate folder with documents.

In the examples below, replace /absolute/path/to/workspace-or-vault with your own path.

3. Connect an MCP client

Claude Desktop, Cursor, OpenCode, and other MCP clients usually use this format:

{
  "mcpServers": {
    "linza": {
      "command": "linza-mcp",
      "env": {
        "LINZA_VAULT": "/absolute/path/to/workspace-or-vault"
      }
    }
  }
}

VS Code / Copilot MCP uses the servers key:

{
  "servers": {
    "linza": {
      "type": "stdio",
      "command": "linza-mcp",
      "env": {
        "LINZA_VAULT": "/absolute/path/to/workspace-or-vault"
      }
    }
  }
}

LINZA_VAULT is not required for startup: without it, the server uses ./vault. For real work, an explicit folder is better.

4. Check startup

linza-mcp --version

After connecting, ask the agent:

Check LINZA with agent_workspace(action="doctor").
Index the folder and show the first 3-5 review intents.

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Embeddings

LINZA can start and show tools without an embedding server. Embeddings are needed for semantic search, topic maps, and relation suggestions.

The simplest local path is LM Studio:

1. Open LM Studio.
2. Download an embedding model, for example text-embedding-granite-embedding-278m-multilingual, nomic-embed-text-v1.5, or another suitable model.
3. Start Local Server.
4. Check that the endpoint is available at http://127.0.0.1:1234/v1.

Example LM Studio variables:

$env:LINZA_EMBED_PROVIDER="lmstudio"
$env:LINZA_EMBED_URL="http://127.0.0.1:1234/v1"
$env:LINZA_EMBED_MODEL="your-embedding-model-name"

Supported providers:

• lmstudio - recommended local mode;
• ollama - local mode through Ollama;
• openai - any OpenAI-compatible endpoint with /embeddings.

If you change provider, model, or dimension, run a full reindex. LINZA checks the embedding signature and stops graph/search workflows if the sidecar is stale or contains mixed vector spaces.

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Main MCP Tools

By default, LINZA shows only 7 MCP tools. That is enough for normal work: check status, index the folder, search, read a file, view counters, diagnose the vault, and guide the agent through agent_workspace.

Tool	Purpose
agent_workspace	One entry point for diagnostics, map, ingest, review, teaching, growth, relations, memory, and context export
guide_next_steps	Show the next safe step in plain language
index_all	Index the Markdown folder into .linza/linza.db
search	Semantic and lexical search
read_file	Read an exact Markdown file
get_stats	Quick service database counters
scan_vault	Folder diagnostic without writing

Low-level tools are implementation details and are available through agent_workspace, so the 7-tool set is a full mode.

agent_workspace Modes

Action	Mode
doctor	Check whether LINZA is ready and what is missing
map	Build a workspace map without writing
teach	Select strong accepted examples for learning
grow	Show or apply growth from accepted examples; dry-run by default
review_next	Show the next review intents; vault intents use rq-*, artifact and workspace intents use aw-*
apply_review_items	Show or apply exact selected IDs; dry-run by default
history	Show accepted and revoked approvals
revoke_approval	Softly revoke an approval without deleting history
ingest_artifacts	Store pasted or extracted material in the sidecar
analyze_inbox	Find events, memory candidates, and knowledge fragments in artifacts
connect	Explain a possible relation between two notes or nodes
search_memory	Search reviewed memory and artifact context
export_context	Build a compact context pack for another agent
record_trace	Store structured traces of agent work, not raw chain-of-thought
analyze_trace	Analyze a stored trace for review
review_calibr	Review calibration lessons derived from traces

For development and audit, a separate low-level mode remains available. Full tool description: Tool Catalog.

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Incoming Artifacts

LINZA can accept material that has not yet become a note:

• pasted text;
• local .md, .txt, .json;
• local .docx, .xlsx;
• local .pdf, if pypdf or PyPDF2 is installed.

LINZA does not browse the web by itself. The agent uses its browser, web-fetch tool, or connector, extracts readable text, and passes it to LINZA as an artifact, for example source_kind="web_article" or source_kind="browser_capture".

Imported text is treated as material for analysis, not as an instruction for the agent. This is the prompt-injection boundary: instructions inside an article, log, chat, or PDF are not executed. Memory, rules, and YAML appear only after review.

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Safety Model

LINZA is a local review-gated sidecar.

Action	Writes To	Changes Note Text?
Indexing, analysis, search	.linza/linza.db	No
Raw artifacts	.linza/linza.db	No
Material format name	.linza/linza.db	No
domains or role in YAML	Only compact YAML after review	No
Hierarchy, causal links, memory, calibration lessons	.linza/linza.db	No
Reports	.linza/reports	No
Context packs	.linza/context-packs	No
write_file	Markdown file only on explicit request	Can create/replace a file, dry-run by default

Additional rules:

• review_next writes nothing;
• apply_review_items is dry-run by default;
• visible YAML edits are compact and require an exact selected ID;
• history shows what was accepted and what was revoked;
• revoke_approval softly revokes an approval: history remains, but active learning and graph helpers ignore it;
• map, teach, grow, and connect stop if source files changed after indexing.

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Agent Instructions

The repository includes a portable operator skill:

agent-pack/skills/linza-operator/SKILL.md
agent-pack/skills/linza-operator/references/workflows.md
agent-pack/skills/linza-operator/references/safety-policy.md
agent-pack/skills/linza-operator/references/tool-audience.md

It explains to an agent how to start with doctor, when to show review intents, how to work with pages through an external browser/web-fetch tool, and why apply actions must first go through dry-run and exact IDs only.

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Stability

LINZA is alpha. The main safety contract should remain stable: indexing, artifact import, search, map, and grow preview do not rewrite source note bodies. Low-level advanced tools and internal code boundaries may still change while the server is being polished.

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Verification

Run the full test suite:

python -m unittest discover -s tests

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Environment Variables

Variable	Required for startup?	Description
LINZA_VAULT	No	Path to the Markdown folder; defaults to ./vault
LINZA_EMBED_PROVIDER	No	lmstudio for the recommended local mode; also openai and ollama
LINZA_EMBED_URL	No	Embeddings API URL; defaults to http://127.0.0.1:1234/v1
LINZA_EMBED_MODEL	No	Embedding model; set before semantic indexing/search
LINZA_EMBED_KEY	No	Optional key for an OpenAI-compatible embeddings API
LINZA_BRIDGE_THRESHOLD	No	Semantic bridge threshold; default 0.55
LINZA_MAX_BRIDGE_PAIRS	No	Maximum note pairs for semantic bridge rebuilds; default 1000000, 0 disables the guard
LINZA_DEFAULT_PROFILE	No	Base search profile name; default general
LINZA_LANGUAGE	No	Language for hints and review route in guide_next_steps: auto, ru, en

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Links

• semiotronika.ru
• PyPI
• GitHub

MIT License (c) 2026 Semiotronika

Cosines are computed. Syntax changes. Semantics remains.