models connected to your Neo4j graph gain incredible flexibility: they’ll generate any Cypher queries through the Neo4j MCP Cypher server. This makes it possible to dynamically generate complex queries, explore database structure, and even chain multi-step agent workflows.
To generate meaningful queries, the LLM needs the graph schema as input: the node labels, relationship types, and properties that outline the information model. With this context, the model can translate natural language into precise Cypher, discover connections, and chain together multi-hop reasoning.
For instance, if it knows about (Person)-[:ACTED_IN]->(Movie) and (Person)-[:DIRECTED]->(Movie) patterns within the graph, it could possibly turn into a sound query. The schema gives it the grounding needed to adapt to any graph and produce Cypher statements which are each correct and relevant.
But this freedom comes at a price. When left unchecked, an LLM can produce Cypher that runs far longer than intended, or returns enormous datasets with deeply nested structures. The result isn’t just wasted computation but additionally a serious risk of overwhelming the model itself. In the mean time, every tool invocation returns its output back through the LLM’s context. Meaning once you chain tools together, all the intermediate results must flow back through the model. Returning hundreds of rows or embedding-like values into that loop quickly turns into noise, bloating the context window and reducing the standard of the reasoning that follows.
For this reason throttling responses matters. Without controls, the identical power that makes the Neo4j MCP Cypher server so compelling also makes it fragile. By introducing timeouts, output sanitization, row limits, and token-aware truncation, we will keep the system responsive and make sure that query results stay useful to the LLM as a substitute of drowning it in irrelevant detail.
The server is accessible on GitHub.
Controlled outputs
So how will we prevent runaway queries and oversized responses from overwhelming our LLM? The reply isn’t to limit what sorts of Cypher an agent can write as the entire point of the Neo4j MCP server is to show the complete expressive power of the graph. As a substitute, we place smart constraints on comes back and a question is allowed to run. In practice, meaning introducing three layers of protection: timeouts, result sanitization, and token-aware truncation.
Query timeouts
The primary safeguard is easy: every query gets a time budget. If the LLM generates something expensive, like a large Cartesian product or a traversal across hundreds of thousands of nodes, it is going to fail fast as a substitute of hanging the entire workflow.
We expose this as an environment variable, QUERY_TIMEOUT, which defaults to 10 seconds. Internally, queries are wrapped in neo4j.Query with the timeout applied. This fashion, each reads and writes respect the identical certain. This alteration alone makes the server way more robust.
Sanitizing noisy values
Modern graphs often attach embedding vectors to nodes and relationships. These vectors might be a whole lot and even hundreds of floating-point numbers per entity. They’re essential for similarity search, but when passed into an LLM context, they’re pure noise. The model can’t reason over them directly, they usually eat an enormous amount of tokens.
To unravel this, we recursively sanitize results with a straightforward Python function. Oversized lists are dropped, nested dicts are pruned, and only values that fit inside an affordable certain (by default, lists under 52 items) are preserved.
Token-aware truncation
Finally, even sanitized results might be verbose. To ensure they’ll at all times fit, we run them through a tokenizer and slice right down to a maximum of 2048 tokens, using OpenAI’s tiktoken library.
encoding = tiktoken.encoding_for_model("gpt-4")
tokens = encoding.encode(payload)
payload = encoding.decode(tokens[:2048])This final step ensures compatibility with any LLM you connect, no matter how big the intermediate data is likely to be. It’s like a security net that catches anything the sooner layers didn’t filter to avoid overwhelming the context.
YAML response format
Moreover, we will reduce the context size further by utilizing YAML responses. In the mean time, Neo4j Cypher MCP responses are returned as JSON, which introduce some extra overhead. By converting these dictionaries to YAML, we will reduce the variety of tokens in our prompts, lowering costs and improving latency.
yaml.dump(
response,
default_flow_style=False,
sort_keys=False,
width=float('inf'),
indent=1, # Compact but still structured
allow_unicode=True,
)Tying it together
With these layers combined — timeouts, sanitization, and truncation — the Neo4j MCP Cypher server stays fully capable but way more disciplined. The LLM can still attempt any query, however the responses are at all times bounded and context-friendly to an LLM. Using YAML as response format also helps lower the token count.
As a substitute of flooding the model with large amounts of information, you come simply enough structure to maintain it smart. And that, in the long run, is the difference between a server that feels brittle and one which feels purpose-built for LLMs.
The code for the server is accessible on GitHub.
