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Restore deleted name

restore_name

Bring back a deleted name (ids from list_deleted_names or name_history). It returns as a NEW name with a new id, its attributes as they were when deleted, put back in the sets it was in and given back its children - where those still exist. Its VALUES ARE NOT RESTORED: deleting a name deletes its values. The response counts what was relinked and what could not be. The restore's provenance records the old id, so the two histories can be followed.

═══ PROVENANCE — required on every write ═══ source: why the name is being restored. agent: your AI agent name (e.g. "Claude").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentYesAI agent name (e.g. "Claude"). REQUIRED.
nameIdYesId the name had before it was deleted
sourceYesWhy it is being restored - ticket, note or document. REQUIRED.
databaseYesDatabase name

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the annotations: it discloses that the restored name returns as a NEW name with a new id, that attributes return as of deletion, sets are relinked, children restored only where they still exist, and critically that VALUES ARE NOT RESTORED. This explains the non-idempotent behavior (new id per restore) that idempotentHint=false only implies, and describes the response's relink counts.

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 the crucial 'VALUES ARE NOT RESTORED' caveat, and every sentence carries information about restore semantics. The separate provenance block is useful but slightly verbose given the schema already marks both fields REQUIRED.

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 mutation tool with no output schema, the description covers inputs, side effects, the key data-loss caveat, provenance requirements, and even what the response reports (relink counts). Nothing an agent needs to call it correctly 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?

Schema description coverage is 100%, so nameId/database/source/agent are already documented. The description adds only the framing that source and agent are provenance required on every write, which the schema already states. Baseline 3 is appropriate when the schema carries the parameter detail.

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+resource ('Bring back a deleted name') and immediately names where the required ids come from ('ids from list_deleted_names or name_history'), routing the agent to the correct sibling tools. An agent can distinguish this from delete_name / name_history / edit_name_attributes without opening a schema.

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?

Explicitly directs the agent to list_deleted_names or name_history for the id and frames the operation as a write requiring provenance. It does not spell out when NOT to restore or contrast with alternatives like deleting-then-recreating, but the context and data-source guidance are clear.

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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