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Update Graph Schema

update_graph_schema
Idempotent

Update a graph project's schema (saves to database, does NOT deploy).

⚠️ Follow ALL rules from create_graph_project: • Must have "nodes" key with at least one entity • Each entity needs "description" and "schema" with field definitions • Each field is {"type": "...", "required": true/false} — required defaults to false • Relationships need "from", "to", and "cardinality" • Field types: string, integer, float, boolean, date, json • Relationship types should be UPPER_SNAKE_CASE • Entity names should be PascalCase

WORKFLOW:

  1. Use get_graph_schema to see current schema

  2. Modify following all rules

  3. Call update_graph_schema (saves only)

  4. Call deploy_graph_staging to apply changes

  5. Monitor with get_job_status

DRY RUN: pass dry_run=true to preview what a deploy WOULD change (renames, deletions) without saving.

NOTE: This only saves the schema. You MUST call deploy_graph_staging afterwards to deploy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaYesNew graph schema with 'nodes' and optionally 'relationships' keys.
dry_runNoPreview the planned migration (renames/deletions) without saving or deploying. Nothing is applied.
project_idYesProject ID (UUID)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / dry_run
      Added value: +{
+  "description": "Preview the planned migration (renames/deletions) without saving or deploying. Nothing is applied.",
+  "type": "boolean"
+}
  2. Added

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=false and destructiveHint=false. The description adds valuable context beyond annotations: it only persists to the database, does not deploy, and dry_run=true previews a migration without saving or deploying. This clarifies side effects and the safe preview path.

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 is structured with a clear opening statement, a concise warning section, bullet-point rules, a numbered workflow, and a final note. Every section gives actionable information, and the most important caveat ('does NOT deploy') is front-loaded.

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

Completeness5/5

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

The description fully equips an agent to call the tool correctly: it explains prerequisites, schema structure rules, dry-run behavior, the save-only side effect, the mandatory next step, and how to monitor results. There is no output schema, so the reference to get_job_status fills that gap.

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

Parameters5/5

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

Although the input schema already describes all three parameters (100% coverage), the description substantially enriches the nested 'schema' parameter by specifying required keys, field defaults, allowed field types, naming conventions, and relationship requirements. This goes well beyond the schema's generic 'New graph schema' description.

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

Purpose5/5

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

The description states a specific verb and resource ('Update a graph project's schema') and immediately clarifies the critical distinction: 'saves to database, does NOT deploy.' It also names the related sibling deploy_graph_staging, so an agent can distinguish this from deploy and other update tools.

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

Usage Guidelines5/5

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

The description provides an explicit WORKFLOW with numbered steps: first call get_graph_schema, then modify, then call this tool, then deploy_graph_staging, then monitor with get_job_status. It includes concrete when-to-use guidance and the exclusion: this tool only saves, so you MUST call deploy_graph_staging afterwards.

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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