Simple test tool to check if MCP server is working.

Ingest and process text into the knowledge graph using semantic analysis.

This tool performs the core knowledge synthesis pipeline:

1. Semantic parsing using Montague Grammar

2. Entity extraction and relationship identification

3. Storage in the Neo4j knowledge graph

4. Automatic insight generation triggers

Args: text: Raw text to process and analyze source: Source identifier for provenance tracking metadata: Additional metadata about the text

Returns: Processing summary with entities and relationships extracted

Trigger autonomous insight generation using the Zettelkasten engine.

This tool activates the autonomous synthesis engine to identify patterns and generate novel insights from the existing knowledge graph.

Args: topic: Optional topic to focus insight generation on confidence_threshold: Minimum confidence level for insights (0.0-1.0)

Returns: Generated insights with confidence scores and evidence trails

Query the knowledge graph for facts and insights using natural language.

This tool provides the conversational interface to the knowledge base, prioritizing synthesized insights over raw facts.

Args: query: Natural language query include_insights: Whether to include AI-generated insights max_results: Maximum number of results to return

Returns: Query results with facts, insights, and reasoning trails

Explore connections and relationships around a specific entity in the knowledge graph.

This tool implements the graph traversal capabilities for discovering non-obvious connections and patterns.

Args: entity: Entity name to explore from depth: How many relationship hops to explore (1-5) connection_types: Specific relationship types to follow

Returns: Visual representation of connections and discovered patterns

Analyze the semantic structure of text using Montague Grammar parsing.

This tool provides insight into the formal semantic analysis capabilities and shows the logical form translations.

Args: text: Text to analyze semantically include_logical_form: Whether to include the formal logical representation

Returns: Semantic analysis with entities, relations, and optional logical forms

List all markdown pages in the wiki vault with pagination.

Args: subdir: Subdirectory to list ('wiki' or 'raw'). limit: Maximum pages to return (default 50, max 200). offset: Offset for pagination. tag: Filter by tag.

Read a wiki page.

Args: path: Relative path from vault root (e.g. 'wiki/concepts/rag.md'). mode: What to return: - "meta": metadata/frontmatter only (from index, 0 file reads) - "excerpt": metadata + first 500 characters of page body (from index) - "full": complete page content (reads file from disk)

Write or update a wiki page with frontmatter.

Args: path: Relative path (e.g. 'wiki/entities/neo4j.md'). body: Markdown body content. summary: One-line summary for the index. tags: Comma-separated tags.

Search wiki pages by keyword.

Args: query: Space-separated search terms. limit: Maximum results to return (default 10). subdir: Scope to subdirectory.

Refresh the wiki index database from disk.

Runs automatically on server start and after write operations. Call manually if files were changed outside Synapse (e.g., Obsidian edits, git pull).

Run a health check on the wiki vault.

Detects orphan pages, broken wikilinks, and missing frontmatter.

Compute HITS hub and authority scores on the wiki wikilink graph.

Authorities = pages cited by many others — load-bearing knowledge nodes. Hubs = pages that link to many good authorities — navigation layers.

Use to identify which pages need deepening (high authority) and which need comprehensive link coverage (high hub).

Cluster wiki pages by semantic similarity using GAAC (TF-IDF).

Identifies:

• Natural topic clusters — pages that belong together

• Missing links — same-cluster pages with no wikilink between them

• Merge candidates — pages so similar they may be redundant (sim > 0.7)

Args: n_clusters: Number of clusters (auto = sqrt of page count if omitted).

Rebuild the wiki index from all wiki pages.

Args: deep: If True, performs a disk-level verification of all indexed files.

Read a raw source file and ingest it into both the knowledge graph and wiki.

Reads from raw/, runs it through the Synapse semantic pipeline, stores in Neo4j, and creates a summary page in wiki/sources/.

Args: filename: Filename inside the raw/ directory.

Fetch a URL with defuddle (clean markdown extraction), save to raw/, ingest into the knowledge graph, and archive to Clippings/.

Use this when researching the web — it strips navigation and clutter, leaving only the article content. Much cleaner than raw web_fetch.

Args: url: The URL to fetch and process. ingest: If True (default), immediately ingest into Neo4j after saving. Set False to save to raw/ only for manual review first.

Record a time-stamped fact in Synapse's episodic memory.

Use this whenever something is worth remembering across sessions: decisions, observations, "Ty said X on date Y", health/diet/symptom log entries, project milestones.

Args: subject: Who or what the fact is about. Free-form name. predicate: The relationship verb. snake_case preferred (e.g. "started_taking", "moved_to", "decided_to_use"). object: The other side of the relation. valid_from: ISO date or datetime when the fact became true. If omitted, "now" is used. Bare dates → midnight UTC. valid_to: ISO date or datetime when the fact stopped being true. Omit for still-current facts. confidence: 0–1. Default 1.0 for explicit user statements; lower when the agent is inferring. source: Where this fact came from. Defaults to "agent:claude" for things Claude is recording. Use "user" or a filename for facts from explicit user statements or document ingestion. note: Free-form context. Stored in metadata for later recall.

Returns: The fact id (stable content hash — safe to call twice).

Look up time-stamped facts about an entity.

Args: entity: Name to look up. Matches subject, object, or both depending on direction. as_of: ISO date/datetime — if given, only facts valid at this point in time are returned. Omit for "currently true" facts. direction: "outgoing" (entity is the subject), "incoming" (entity is the object), or "both" (default).

Returns: Newline-separated list of facts with timestamps. Empty if none found.

Chronological view of remembered facts.

Args: entity: Scope to one entity, or None for the global timeline. limit: Max number of rows. Default 50.

Returns: Time-ordered fact list, oldest first.

Mark a previously-recorded fact as no longer true.

Sets valid_to rather than deleting — the historical record stays intact, but the fact is no longer "currently true" for default queries.

Args: subject/predicate/object: The triple to invalidate. ended: ISO date/datetime when the fact stopped being true. Defaults to now if omitted.

Returns: Number of facts affected.

Find candidate causes by temporal correlation.

Surfaces facts whose valid_from falls in the window [before - within_days, before] and that share at least one entity with facts about effect_entity.

This is exactly the "track everything you ate to find what caused the headaches" pattern — you record symptom onset, you record meals and medications, then this tool surfaces co-occurring events as candidates. The tool returns correlation; the human (or a downstream reasoning step) decides what caused what.

Args: effect_entity: The thing whose causes you're hunting (e.g. "headache", "rash", "build failure"). before: ISO date/datetime — when the effect was observed. within_days: How far back to search. Default 30.

Returns: Ranked list of candidate cause-effect pairings with day deltas.

Quick stats: how many temporal facts are stored, time span covered.