MCP Server - Semantica

semantica.mcp_server exposes Semantica’s knowledge graph, decision intelligence, semantic extraction, and reasoning capabilities as an MCP (Model Context Protocol) server over stdio:

12 MCP tools exposed: extract entities, query graph, record decisions, run reasoning, export results
No Python code required after launch: configure once, use from any MCP-aware client
Compatible with Claude Desktop, Windsurf, Cline, Continue, VS Code, Roo Code, Cursor

Server Interface

// Configure in your MCP client (Claude Desktop, Windsurf, Cursor, VS Code, etc.)
{
  "mcpServers": {
    "semantica": {
      "command": "semantica-mcp"
    }
  }
}

# Or run directly
semantica-mcp
# or
python -m semantica.mcp_server

semantica.mcp_server is a stdio server process, not a Python library. It exposes no importable classes: all interaction happens through MCP tool calls from a connected AI client.

The server communicates over stdio: don’t add logging to stdout. Any print() or logger output directed to stdout will corrupt the JSON-RPC message stream. All logging is written to stderr only. Configure log verbosity with the SEMANTICA_LOG_LEVEL environment variable.

What You Get

12 MCP Tools

Extract entities, extract relations, record decisions, query decisions, find precedents, trace causal chains, add entities, add relationships, run analytics, summarise graph, run reasoning, export graph.

3 Readable Resources

Live graph JSON (semantica://graph/summary), decision list, and schema/version info: readable by any MCP client.

Zero Infrastructure

Runs over stdio: no server, no port, no Docker required. One config block to activate in any MCP client.

Persistent Graphs

Point SEMANTICA_KG_PATH at a saved graph file to reload it automatically on every server startup.

Decision Intelligence

Record decisions, find precedents via hybrid similarity search, and trace causal chains across agent runs.

REST Alternative

The Explorer module offers a full HTTP API and browser dashboard if you prefer programmatic access.

Installation

pip install semantica

The MCP server is included in the base install: no extras required.

Configuration

Find your MCP client's settings file

Client	Settings file
Claude Desktop (macOS)	`~/Library/Application Support/Claude/claude_desktop_config.json`
Claude Desktop (Windows)	`%APPDATA%\Claude\claude_desktop_config.json`
Cursor	`.cursor/mcp.json` in your project, or `~/.cursor/mcp.json` globally
VS Code / Continue	`.vscode/mcp.json` or user settings
Windsurf / Cline / Roo Code	App-specific settings → MCP Servers

Add the Semantica MCP server config

{
  "mcpServers": {
    "semantica": {
      "command": "semantica-mcp"
    }
  }
}

Configure your MCP client’s command field exactly. The command field must point to the exact executable path (use which semantica-mcp on macOS/Linux to find it). A wrong path fails silently: the server just doesn’t appear in the tools list. Test with the raw echo | semantica-mcp command first to confirm the binary works.

Test locally before configuring your client

# Run the server directly (reads from stdin, writes to stdout)
semantica-mcp

# Or via Python module
python -m semantica.mcp_server

# Send a JSON-RPC initialize message to confirm it's working
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}' | semantica-mcp

Environment Variables

Variable	Default	Description
`SEMANTICA_KG_PATH`	(none: in-memory graph)	Path to a persisted graph file to load on startup
`SEMANTICA_LOG_LEVEL`	`WARNING`	Log verbosity: `DEBUG`, `INFO`, `WARNING`

The graph starts empty unless you set SEMANTICA_KG_PATH. The MCP server creates a fresh in-memory ContextGraph on first use. Set SEMANTICA_KG_PATH to a previously saved graph file to restore state across server restarts. Without it, all data is lost when the process exits.

Enable debug logging for troubleshooting. Set SEMANTICA_LOG_LEVEL=DEBUG in your MCP client’s env block, or run python -m semantica.mcp_server directly and inspect stderr output.

Tools

The MCP server exposes 12 tools that any connected AI assistant can call:

Tool	Category	Description
`extract_entities`	Extraction	NER: find people, places, organisations, concepts
`extract_relations`	Extraction	Typed relation and triplet extraction
`record_decision`	Decision Intelligence	Save a decision with reasoning and outcome
`query_decisions`	Decision Intelligence	Search recorded decisions by natural language or category
`find_precedents`	Decision Intelligence	Hybrid similarity search over past decisions
`get_causal_chain`	Decision Intelligence	Trace upstream / downstream causal chains
`add_entity`	Graph Operations	Add a node to the live graph
`add_relationship`	Graph Operations	Add a directed edge between two nodes
`get_graph_summary`	Graph Operations	Node count, decision count, graph status
`get_graph_analytics`	Graph Operations	PageRank centrality and community detection
`run_reasoning`	Reasoning	Forward-chain IF/THEN rules over facts
`export_graph`	Reasoning & Export	Serialise the graph (`turtle`/`ttl`: RDF Turtle aliases, `nt`, `xml`, `json-ld`, `json`)

Knowledge Extraction

extract_entities

Extract named entities (people, places, organisations, concepts) from text using Semantica NER.Input:

{ "text": "Apple Inc. was founded by Steve Jobs in Cupertino in 1976." }

Output:

{
  "entities": [
    { "label": "Apple Inc.", "type": "ORGANIZATION", "start": 0,  "end": 10,  "confidence": 0.98 },
    { "label": "Steve Jobs", "type": "PERSON",       "start": 26, "end": 36,  "confidence": 0.99 },
    { "label": "Cupertino",  "type": "LOCATION",     "start": 40, "end": 49,  "confidence": 0.97 },
    { "label": "1976",       "type": "DATE",          "start": 53, "end": 57,  "confidence": 0.95 }
  ],
  "count": 4
}

extract_relations

Extract typed relations and (subject, predicate, object) triplets from text.Input:

{ "text": "Steve Jobs founded Apple Inc. and led it until 2011." }

Output:

{
  "relations": [
    { "source": "Steve Jobs", "type": "founded", "target": "Apple Inc.", "confidence": 0.96 }
  ],
  "triplets": [
    { "subject": "Steve Jobs", "predicate": "founded", "object": "Apple Inc." }
  ],
  "relation_count": 1,
  "triplet_count": 1
}

Decision Intelligence

record_decision

Record a decision with full context, reasoning, and metadata into the knowledge graph.Input:

{
  "category": "model_selection",
  "scenario": "Choose LLM for production reasoning pipeline",
  "reasoning": "GPT-4 benchmark advantage justifies 3x cost increase",
  "outcome": "selected_gpt4",
  "confidence": 0.91,
  "decision_maker": "product_team",
  "valid_from": "2024-01-01",
  "valid_until": "2024-12-31"
}

Required fields: category, scenario, reasoning, outcome, confidence. Optional: decision_maker (defaults to "mcp_client"), valid_from, valid_until.Output:

{ "decision_id": "dec_a1b2c3", "status": "recorded" }

query_decisions

Query recorded decisions by natural language or category filter.Input:

{ "query": "model selection", "category": "model_selection", "limit": 5 }

All fields are optional. limit defaults to 10. When query is provided, similarity search is used. When omitted, category filter applies.

find_precedents

Find past decisions similar to a given scenario using hybrid similarity search.Input:

{ "scenario": "Choose cloud provider for HIPAA workload", "max_results": 5 }

max_results defaults to 5, maximum 50.

Use find_precedents before high-stakes decisions. The tool performs hybrid similarity search across all recorded decisions. Call it at the start of any significant decision path: it surfaces past reasoning that may be directly applicable, reducing redundant work and improving consistency across agent runs.

get_causal_chain

Trace the causal chain upstream or downstream from a decision.Input:

{ "decision_id": "dec_a1b2c3", "direction": "downstream", "max_depth": 5 }

direction accepts "upstream" or "downstream" (default: "downstream"). max_depth defaults to 5, maximum 20.

Graph Operations

add_entity

Add a node/entity to the live knowledge graph.Input:

{
  "id": "apple_inc",
  "label": "Apple Inc.",
  "type": "Organization",
  "metadata": { "founded": 1976, "hq": "Cupertino" }
}

Only id is required. label defaults to the id value. type defaults to "Entity".

add_relationship

Add a directed relationship (edge) between two existing entities.Input:

{
  "source": "steve_jobs",
  "target": "apple_inc",
  "type": "FOUNDED",
  "metadata": { "year": 1976 }
}

source and target are required. type defaults to "RELATED_TO".

get_graph_summary

Return a high-level summary of the current knowledge graph.Output:

{
  "node_count": 42,
  "decision_count": 5,
  "graph_ready": true
}

Takes no input parameters.

get_graph_analytics

Compute PageRank centrality and community detection over the current graph. Returns top nodes by PageRank, community count, and overall node/edge counts.Takes no input parameters.

Reasoning

run_reasoning

Run forward-chaining IF/THEN rules over a set of facts to derive new facts.Input:

{
  "facts": ["Employee(John)", "Manager(John)"],
  "rules": ["IF Manager(?x) THEN HasAuthority(?x)"]
}

Output:

{ "derived_facts": ["HasAuthority(John)"] }

Export

export_graph

Export the current knowledge graph to a serialisation format.Input:

{ "format": "json-ld" }

Supported formats: turtle, ttl, nt, xml, json-ld, json. Default is json-ld.

Resources

The MCP server exposes three readable resources:

URI	Description
`semantica://graph/summary`	High-level graph statistics
`semantica://decisions/list`	All recorded decisions (up to 50)
`semantica://schema/info`	Server version and available tools

Context

The ContextGraph that the MCP server operates on.

Semantic Extract

NER and relation extraction powering the MCP tools.

Reasoning

Forward-chaining engine behind run_reasoning.

Agno Integration

Use Semantica inside Agno multi-agent teams.

​Server Interface

​What You Get

12 MCP Tools

3 Readable Resources

Zero Infrastructure

Persistent Graphs

Decision Intelligence

REST Alternative

​Installation

​Configuration

​Environment Variables

​Tools

​Knowledge Extraction

​Decision Intelligence

​Graph Operations

​Reasoning

​Export

​Resources

Context

Semantic Extract

Reasoning

Agno Integration

Server Interface

What You Get

Installation

Configuration

Environment Variables

Tools

Knowledge Extraction

Decision Intelligence

Graph Operations

Reasoning

Export

Resources