MCP Server

What Is MCP?

MCP stands for the Model Context Protocol. It is an open standard that allows external AI assistants (like Claude Desktop, Cursor, or Windsurf) to securely access local tools and data sources. The Semantica MCP server exposes your knowledge graph as 12 callable tools. By connecting it, any compatible AI client can traverse the graph live, record decisions, run analytics, and export results during a conversation — without you having to write custom tool wrappers.

The Semantica MCP server exposes 12 tools and 3 read-only resources. All tools accept and return JSON. No configuration beyond an optional environment variable for graph persistence is required.

Architecture & Communication

It is important to understand how MCP works under the hood. The Semantica MCP server is not a REST API. There are no network ports, no HTTP endpoints, and no API keys required. Instead, the AI client launches semantica-mcp locally as a subprocess. All communication between the AI and Semantica happens securely through standard input and output (stdio). Because the server runs locally under your user account, it inherently has your local file permissions.

Why Use MCP With Semantica?

Zero-Code Integration: Instantly connect Semantica’s graph capabilities to your favorite AI IDE or desktop chat app without writing any glue code.
Real-Time Graph Updates: Chat with an AI to extract entities from documents and watch them populate your live knowledge graph instantly.
Auditable AI: Use the AI to make decisions and have it automatically record the reasoning and causal chain directly into the graph via Semantica’s decision intelligence tools.

When To Use / When Not To Use

When to Use: You want to use a third-party AI interface (like Claude Desktop or Windsurf) to manipulate, query, and reason over a Semantica knowledge graph on your local machine.
When NOT to Use: You are building an autonomous Python script or backend service. If you are writing Python code to build an agent, use semantica.context.AgentContext natively instead of spinning up an MCP server. The MCP server does not support remote hosting over HTTP/SSE.

Typical Workflow

Connecting your AI client follows a standard progression:

Install: Install Semantica in your Python environment.
Configure Client: Add the semantica-mcp command and absolute graph paths to your AI client’s JSON configuration.
Start Client: Launch Claude Desktop or Windsurf, which automatically spawns the MCP server.
Tool Calls: Prompt the AI in natural language. The AI autonomously chains the 12 available tools.
Graph Updates: The AI directly modifies your local graph, adding entities, edges, and decisions.

Starting the Server

Install Semantica, then configure your client to launch the MCP server. The server runs using the stdio transport.

pip install semantica

# Via the CLI entry-point (recommended)
semantica-mcp

# Or via the Python module directly
python -m semantica.mcp_server

By default, the server logs at WARNING level and produces no startup output. Set SEMANTICA_LOG_LEVEL=INFO (or DEBUG) to see startup messages on stderr. Without SEMANTICA_KG_PATH the server initialises an empty in-memory graph — sufficient for testing. For a persistent graph that survives restarts, set the path:

SEMANTICA_KG_PATH=/data/threat_graph.json semantica-mcp

Without SEMANTICA_KG_PATH, the graph resets when the server process exits. Always set this path using an absolute file path for any session whose data should survive a restart.

Connecting to Claude Desktop

Edit the Claude Desktop config file — on macOS at ~/Library/Application Support/Claude/claude_desktop_config.json, on Windows at %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "semantica": {
      "command": "semantica-mcp",
      "env": {
        "SEMANTICA_KG_PATH": "/absolute/path/to/knowledge_graph.json",
        "SEMANTICA_LOG_LEVEL": "INFO"
      }
    }
  }
}

Restart Claude Desktop after saving. The Semantica tools appear in the tool palette automatically — Claude can now call them during any conversation. If semantica-mcp is not on your system PATH (for example, if it is installed in a virtualenv), use the full absolute binary path in "command": "/path/to/venv/bin/semantica-mcp".

Connecting to Other Clients

Windsurf — in Settings → MCP Servers → Add Server, or edit ~/.windsurf/mcp_servers.json:

{
  "semantica": {
    "command": "semantica-mcp",
    "env": { "SEMANTICA_KG_PATH": "/absolute/path/to/knowledge_graph.json" }
  }
}

VS Code (Cline / Roo Code / Continue) — add to .mcp.json in your project root:

{
  "servers": {
    "semantica": {
      "type": "stdio",
      "command": "semantica-mcp",
      "env": {
        "SEMANTICA_KG_PATH": "${workspaceFolder}/knowledge_graph.json",
        "SEMANTICA_LOG_LEVEL": "DEBUG"
      }
    }
  }
}

Docker:

docker run --rm -i \
  -e SEMANTICA_KG_PATH=/data/kg.json \
  -v /local/absolute/path:/data \
  ghcr.io/semantica-agi/semantica-mcp:latest

What the Agent Can Do: The 12 Tools

Once connected, the LLM can call any of these tools during a conversation. The agent chains them automatically — you do not orchestrate the sequence, you just describe what you want. Entity and relation extraction — extract_entities pulls named entities from free text; extract_relations extracts semantic relationships and RDF triplets. These two tools turn a raw OSINT report into structured graph inputs without any preprocessing. Knowledge graph manipulation — add_entity adds a node, add_relationship adds a directed edge. After extraction, the agent calls these to persist what it found into the live graph. Decision intelligence — record_decision writes a decision as a provenance node with confidence score, reasoning, and decision maker identity. query_decisions retrieves past decisions by query or category. find_precedents finds the most similar past decisions by semantic similarity. get_causal_chain traces decision causality upstream or downstream. Reasoning — run_reasoning applies forward-chaining IF/THEN rules over a set of facts and returns derived conclusions. Analytics and export — get_graph_analytics computes PageRank centrality and community detection. get_graph_summary returns node count, decision count, and server status. export_graph serializes the current graph to Turtle ("turtle" / "ttl"), RDF/XML ("xml"), N-Triples ("nt"), JSON-LD ("json-ld"), or plain JSON ("json").

Universal Example: Employee Directory

Before diving into complex domain examples, here is a simple, universally understood session. An HR manager types a prompt into Claude Desktop:

“Extract entities from this meeting transcript about Alice transferring to Engineering, add them to the graph, and record a promotion decision.”

Claude chains four tool calls automatically:

1. extract_entities(text="Alice is transferring to Engineering...")
   → { "entities": [{"label": "Alice", "type": "Employee"}, {"label": "Engineering", "type": "Department"}] }

2. add_entity(id="emp-alice", label="Alice", type="Employee")
   add_entity(id="dept-eng", label="Engineering", type="Department")

3. add_relationship(source="emp-alice", target="dept-eng", type="WORKS_IN")

4. record_decision(
       category="promotion",
       scenario="Alice transferring to Engineering",
       reasoning="Approved by Engineering Director",
       outcome="transfer_approved",
       confidence=1.0
   )

The graph is updated instantly with the new organizational structure and a fully auditable decision trail.

Watching a Real Agent Session

Here is what happens when a cybersecurity analyst types a prompt into Claude Desktop and the graph is live. The prompt is:

“Extract entities and relationships from this OSINT report, add them to the knowledge graph, then record an attribution decision for APT29 with confidence 0.88 and export the full graph as Turtle.”

Claude chains six tool calls automatically:

1. extract_entities(text="<report text>")
   → { "entities": [{"label": "APT29", "type": "ThreatActor"}, ...] }

2. extract_relations(text="<report text>")
   → { "relations": [{"source": "APT29", "type": "EXPLOITS", "target": "CVE-2024-3400"}] }

3. add_entity(id="apt29", label="APT29", type="ThreatActor", metadata={"alias": "NOBELIUM"})
   → { "status": "added", "id": "apt29" }
   (repeated for each extracted entity)

4. add_relationship(source="apt29", target="cve-2024-3400", type="EXPLOITS",
                    metadata={"confidence": 0.97})
   → { "status": "added" }
   (repeated for each extracted relation)

5. record_decision(
       category="threat_attribution",
       scenario="C2 beacon from 185.220.101.47, TTP T1566.001 observed",
       reasoning="IP overlaps APT29 infrastructure cluster; TTPs match NOBELIUM phishing playbook",
       outcome="attributed_to_apt29",
       confidence=0.88,
       decision_maker="analyst_zhang"
   )
   → { "decision_id": "dec_a3f2b1", "status": "recorded" }

6. export_graph(format="turtle")
   → { "format": "turtle", "data": "@prefix ... <apt29> a :ThreatActor ..." }

The analyst asked one question in natural language. Six structured tool calls happened against live graph data. The result is a populated graph, a recorded attribution decision with full provenance, and a Turtle export ready for the SPARQL endpoint — all in a single conversation turn.

The Three Read-Only Resources

Resources expose graph state without a tool call — the client can read them at any point:

URI	Description
`semantica://graph/summary`	Node count, decision count, server status
`semantica://decisions/list`	Up to 50 most recent recorded decisions
`semantica://schema/info`	Server version, capabilities, available tool list

Domain Examples

Defense — CTI/Threat
Security — SOC/Incident
Life Science — Clinical/Pharma
Banking — Risk/Compliance

The CTI team uses Claude Desktop to correlate new OSINT reports against the existing threat graph, record attribution decisions, and query causal chains — all through natural language, with the graph updated live.Analyst prompt:

“Extract entities from this Mandiant report on APT40, add them to the knowledge graph, find any past decisions about APT40 attribution, and record a new attribution decision with confidence 0.84.”

Claude chains automatically:

extract_entities + extract_relations on the report text
add_entity for each extracted entity (APT40, CVEs, infrastructure nodes)
add_relationship for each extracted relation
query_decisions(query="APT40 attribution", category="threat_attribution")
find_precedents(scenario="APT40 targeting maritime sector", max_results=3)
record_decision(category="threat_attribution", outcome="attributed_to_apt40", confidence=0.84, decision_maker="analyst_zhang")

The attribution is now a graph node linked to the OSINT evidence, searchable by future agents.

{
  "category": "threat_attribution",
  "scenario": "C2 infrastructure overlaps APT40 cluster; TEMP.Periscope TTPs confirmed",
  "reasoning": "Three C2 IPs match known APT40 hosting ASN; T1190 exploit chain identical to 2023 campaign",
  "outcome": "attributed_to_apt40",
  "confidence": 0.84,
  "decision_maker": "analyst_zhang"
}

During a live incident, the SOC uses Claude to reason over the graph, apply zero-trust policy rules, and record containment decisions with their causal chain — creating a real-time audit trail.SOC analyst prompt:

“Add WKSTN-047 and DC01 as hosts, add the lateral movement relationship between them, run reasoning to classify the severity, and record a containment decision.”

Claude chains:

add_entity(id="wkstn-047", type="Host", label="WKSTN-047")
add_entity(id="dc01", type="Host", label="DC01")
add_relationship(source="wkstn-047", target="dc01", type="lateral_movement")
run_reasoning(facts=["Host(WKSTN-047)", "LateralMove(WKSTN-047, DC01)", "DC(DC01)"], rules=["IF LateralMove(X, Y) AND DC(Y) THEN CriticalIncident(X)"])
record_decision(category="containment", scenario="Lateral movement to DC detected", outcome="isolate_wkstn047", confidence=0.95)
get_causal_chain(decision_id="...", direction="downstream", max_depth=3)

The containment decision and its downstream effects are captured in the graph for the post-mortem.

Clinical AI assistants use the MCP server to record treatment decisions with provenance, retrieve guideline precedents, and export decision graphs for regulatory submission and MDT review.Clinical prompt:

“The patient has eGFR 28 and is on metformin. Find precedents for metformin dose modification with severely reduced kidney function, then record a treatment modification decision.”

Claude calls:

find_precedents(scenario="metformin with eGFR below 30", max_results=5)
record_decision(category="treatment_modification", scenario="eGFR 28, current metformin 1000mg BD", reasoning="eGFR 28 is below the 30 mL/min/1.73m2 absolute contraindication threshold per BNF and NICE NG28", outcome="discontinue_metformin_switch_to_gliclazide", confidence=0.97, decision_maker="clinical_ai_v2")
get_causal_chain(direction="upstream") — surfaces the guideline node that drove the decision
export_graph(format="json-ld") — produces the decision graph for MDT review

The graph captures the decision, its guideline basis, and the causal chain — all retrievable for regulatory audit.

Credit risk teams use the MCP server to record every lending decision with its reasoning chain, surface regulatory precedents, and export compliance graphs for Basel III model governance review.Credit analyst prompt:

“Record a conditional mortgage approval for APP-2025-994421 (LTV 78%, DSTI 38%, credit score 714), find the three most similar past approvals, and return the causal chain for this decision.”

Claude calls:

record_decision(category="mortgage_origination", scenario="LTV 78%, DSTI 38%, credit score 714, first-time buyer", reasoning="LTV within 80% cap; DSTI 38% under stressed rate scenario breaches 35% guideline — conditional approval with LMI requirement", outcome="approved_conditional_lmi", confidence=0.89, decision_maker="credit_model_v3")
find_precedents(scenario="mortgage approval borderline DSTI stress test", max_results=3)
get_causal_chain(decision_id="...", direction="upstream", max_depth=5)
export_graph(format="turtle") — produces the decision provenance graph for model governance committee

The result is a fully auditable credit decision trail with precedent links, ready for SR 11-7 model risk governance review.

Common Pitfalls

Treating MCP as an HTTP server: Do not try to curl the MCP server or look for a port number. It communicates via stdin/stdout and waits for JSON-RPC messages from the parent AI client.
Using relative paths for SEMANTICA_KG_PATH: Because the AI client spawns the server as a subprocess, the working directory can be unpredictable. Always use absolute paths (e.g., C:\Users\Name\graph.json or /Users/name/graph.json) to avoid losing your data.
Virtual environment PATH issues: If you installed Semantica inside a Python virtual environment, Claude Desktop will not automatically find semantica-mcp on the global system PATH. You must provide the absolute path to the binary in the "command" field.
Expecting remote hosting support: Stdio-based MCP servers must run on the same local machine as the AI client. Remote execution over a network is not supported.
Confusing MCP integration with AgentContext: If you are writing your own Python code to orchestrate an LLM, do not use the MCP server. Use the AgentContext class natively within your code.

Troubleshooting

Server does not appear in Claude Desktop — fully quit and reopen Claude Desktop after editing the config (closing the window is not enough). Verify the binary is on PATH: which semantica-mcp on Unix, where semantica-mcp on Windows. If using a virtualenv, use the absolute binary path in "command". Set SEMANTICA_LOG_LEVEL=DEBUG and check stderr for startup errors. Graph data not persisting between sessions — set SEMANTICA_KG_PATH to an absolute file path. Without it, the graph is in-memory only and resets on every server restart. Tool calls returning empty results — get_graph_summary returning "node_count": 0 means the graph is empty. Populate it via add_entity and add_relationship, or run extract_entities on text first and then add_entity for each result. Permission errors on SEMANTICA_KG_PATH — the server process needs read/write access to the file and its parent directory. If running inside Docker, verify the volume mount and file ownership.

Reasoning & Rules — the engine behind the run_reasoning tool
Decision Intelligence — how decisions are stored as causal graph nodes
Context Graphs — the graph that add_entity and add_relationship write to
Export & Serialization — all export formats available via export_graph
Ontology Management — generate OWL ontologies from the graph built via MCP

Overview

Core Concepts

Guides

Integrations

Graph Stores

Vector Stores

What Is MCP?

Architecture & Communication

Why Use MCP With Semantica?

When To Use / When Not To Use

Typical Workflow

Starting the Server

Connecting to Claude Desktop

Connecting to Other Clients

What the Agent Can Do: The 12 Tools

Universal Example: Employee Directory

Watching a Real Agent Session

The Three Read-Only Resources

Domain Examples

Common Pitfalls

Troubleshooting

​What Is MCP?

​Architecture & Communication

​Why Use MCP With Semantica?

​When To Use / When Not To Use

​Typical Workflow

​Starting the Server

​Connecting to Claude Desktop

​Connecting to Other Clients

​What the Agent Can Do: The 12 Tools

​Universal Example: Employee Directory

​Watching a Real Agent Session

​The Three Read-Only Resources

​Domain Examples

​Common Pitfalls

​Troubleshooting

​Related Guides

What Is MCP?

Architecture & Communication

Why Use MCP With Semantica?

When To Use / When Not To Use

Typical Workflow

Starting the Server

Connecting to Claude Desktop

Connecting to Other Clients

What the Agent Can Do: The 12 Tools

Universal Example: Employee Directory

Watching a Real Agent Session

The Three Read-Only Resources

Domain Examples

Common Pitfalls

Troubleshooting

Related Guides