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RationalBloks

by io.github.rationalbloks
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Deploy Graph to Production

deploy_graph_production

Promote graph staging to production. Creates a separate production Neo4j instance with its own credentials and database. Requires paid plan. A plan that drops data is refused, naming every table, field, entity or relationship it would drop, and every field whose type change rounds or cuts its values (a decimal with a smaller scale or turned integer, a datetime turned date): deploy_destructive applies it, a tool of its own so a client asks before it runs. One operation runs on a project at a time: while another runs, the call is refused and the refusal names the running job; wait for it with get_job_status, then call again. While RationalBloks is being updated, the call is refused with 'RationalBloks is being updated': call it again in a few minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesProject ID (UUID)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / properties / confirm_destructive
      Removed value: -{
-  "description": "Set true only after reviewing the plan: a deploy that drops tables, columns, entities, relationships or fields is refused without it",
-  "type": "boolean"
-}
  2. Changed1 schema field changed
    • addedInput schema / properties / confirm_destructive
      Added value: +{
+  "description": "Set true only after reviewing the plan: a deploy that drops tables, columns, entities, relationships or fields is refused without it",
+  "type": "boolean"
+}
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only cover the coarsest safety flags; the description goes far beyond them, disclosing refusal semantics (destructive plans are refused with a full enumeration of dropped items and lossy type changes), single-operation-per-project serialization, and the literal maintenance error string. This is exactly the behavioral context annotations cannot carry.

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

Conciseness4/5

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

Front-loaded with the core action and effect before the long tail of refusal cases. Dense and mostly earned, though the final maintenance-retry sentence is somewhat verbose and could be tighter without losing meaning.

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?

For a one-parameter deployment tool with no output schema, the description covers what an agent needs: prerequisites, the destructive-plan escape hatch, concurrency limits, and retry triggers with the exact tool to poll. Nothing material is missing.

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

Parameters3/5

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

One parameter with 100% schema description coverage ('Project ID (UUID)'), so the schema already carries the semantics. The description adds nothing parameter-specific, which is the expected baseline when the schema does the work.

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?

States a specific verb and resource ('Promote graph staging to production') and immediately clarifies the effect: a separate production Neo4j instance with its own credentials and database. This clearly distinguishes it from deploy_graph_staging and deploy_production.

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?

Explicitly states prerequisites (requires paid plan), routes dropping plans to the sibling deploy_destructive, and gives the exact when-to-retry conditions (concurrent job → wait via get_job_status then call again; maintenance window → retry in a few minutes). Nothing is left to inference.

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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