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reactome-mcp

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by reactome

reactome_cypher_query

Query the Reactome Neo4j graph database with read-only Cypher statements to analyze biological pathways, while safeguards prevent writes and cap result sizes.

Instructions

Run a Cypher query against the local Reactome Neo4j graph database. The session runs in READ mode, which rejects native write clauses (CREATE/MERGE/DELETE/SET/REMOVE). APOC procedures that can write through that guardrail (apoc.cypher.runWrite, apoc.periodic., apoc.create/merge/refactor., apoc.load/import/export., apoc.trigger., apoc.nodes.delete) are rejected before execution. Row count, per-row size, and total response size are capped; a query timeout terminates runaway queries. Use LIMIT and project specific fields in your query for large results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesCypher query to execute (read-only; max 50000 chars)
paramsNoOptional parameter map passed to the query
max_rowsNoMaximum rows to return (default 100, cap 1000)
max_row_charsNoMaximum JSON chars per row before replacing with a summary (default 2000, cap 20000)
max_total_charsNoMaximum total JSON chars across all rows before truncation (default 40000, cap 200000)
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility. It thoroughly discloses read-only enforcement, rejected APOC write procedures, row/size caps, and query timeout, which is exactly the kind of behavioral detail an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses five sentences, each adding critical safety or usage information, without redundant fluff. It front-loads the core action and then layers constraints logically.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a raw Cypher tool with no output schema, the description covers input constraints, safety, and practical guidance. It does not detail error responses, but given the unpredictable nature of Cypher results, the provided information is comprehensive enough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all five parameters with descriptions (100% coverage). The description adds value by connecting the caps to query design and introducing the timeout behavior, which goes slightly beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Run a Cypher query against the local Reactome Neo4j graph database,' clearly identifying the tool's verb and resource. It does not explicitly distinguish from sibling tools like reactome_cypher_schema or reactome_query, but the specificity of the action makes the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context about read-only mode and result caps, and explicitly advises 'Use LIMIT and project specific fields in your query for large results.' It does not name alternatives or state when not to use this tool, but the context is sufficient for an agent to infer appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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