Skip to main content
Glama

update_schema_incremental

Add new fields to an entity schema and immediately activate the updated version; optionally migrate existing raw_fragments to observations.

Instructions

Incrementally update a schema by adding new fields from raw_fragments or agent recommendations. Creates new schema version and activates it immediately, so all new data stored after this call will use the updated schema. Optionally migrates existing raw_fragments to observations for historical data backfill.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_typeYesEntity type to update
fields_to_addNoFields to add to schema
fields_to_removeNoField names to remove from schema (triggers major version bump). Observation data is preserved; fields can be restored by re-adding them later.
schema_versionNoNew schema version (auto-increments if not provided)
user_specificNoCreate user-specific schema variant (default: false)
user_idNoUser ID for user-specific schema (required if user_specific=true)
activateNoActivate schema immediately so it applies to new data (default: true). If false, schema is registered but not active.
migrate_existingNoMigrate existing raw_fragments to observations for historical data backfill (default: false). Note: New data automatically uses updated schema after activation, migration is only for old data.
Behavior4/5

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

Behavioral traits like immediate activation and optional historical backfill are disclosed. The description does not cover removal behavior (major version bump) but the schema documents it, so this is acceptable. No annotation contradiction.

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 concise with two sentences, front-loading the purpose and each sentence adding value without redundancy.

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?

Given the comprehensive schema and no output schema, the description covers the main use case and effects. It could mention return value but is adequate for informed use.

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 coverage is 100%, so baseline 3. The description adds minimal extra meaning beyond what the schema already provides.

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 clearly states the tool updates a schema incrementally by adding fields from raw_fragments or agent recommendations, creating a new version and activating it. However, it does not explicitly distinguish from sibling tools like register_schema.

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

Usage Guidelines3/5

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

The description implies usage when incremental schema updates are needed, but does not provide explicit guidance on when not to use it or alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/markmhendrickson/neotoma'

If you have feedback or need assistance with the MCP directory API, please join our Discord server