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Glama

datamodel.get_datamodel_shares

datamodel_get_datamodel_shares
Read-only

Retrieve all user and group shares for a data model, resolving IDs to names and emails with EDIT, READ, or USE permissions. Use to audit who has access to a model.

Instructions

Retrieve all share entries (users and groups) for a given data model. Resolves user and group identifiers to names/emails and returns the shares in a flat row format. Permission codes are mapped to "EDIT", "READ", or "USE". Returns: list[dict[str, Any]] List of dicts, each with "datamodel_name", "datamodel_id", "party_name", "party_type", and "permission" — an empty list means the model genuinely has no shares. On failure, …

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datamodel_nameYesName of the data model to retrieve shares for.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior5/5

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

The annotations only mark it read-only and non-destructive. The description goes further by disclosing that user/group identifiers are resolved to names/emails, permission codes are normalized to EDIT/READ/USE, results are returned as flat rows, and an empty list means genuinely no shares rather than failure.

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

Conciseness3/5

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

The description is front-loaded and mostly information-dense, but it ends with the dangling phrase 'On failure, …', which makes it look truncated and weakens the overall structure.

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?

It provides necessary return-shape details—field names, permission mapping, and empty-list semantics—which is especially important because there is no output schema. It falls short only on failure behavior and prerequisites, and the truncated closing sentence leaves a small but real gap.

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%, and the schema already documents datamodel_name as 'Name of the data model to retrieve shares for.' The description does not add constraints, formats, or examples beyond that, so the baseline of 3 applies.

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: 'Retrieve all share entries (users and groups) for a given data model.' It also adds distinguishing scope—users/groups, permission mapping, flat row format—that separates it from sibling tools like dashboard shares.

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 intended use is implied: use when you need all share entries for a specific data model. However, it does not explicitly discuss when to choose this over related tools such as datamodel_get_datasecurity_detail or the access-management share/dashboard tools, nor does it state exclusions.

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