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Get Model register

model_get

Hydrate records from MySQL. include=summary (default counts), dictionaries, parameters, observations, edges, or all. Prefer model_upsert_observation to change one fact — do not treat this as a blob to rewrite.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNo
includeNoDefault summary

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the burden, and it does provide meaningful behavioral context: data comes from MySQL, include controls what gets hydrated, summary defaults to counts, and this tool should not be treated as a rewrite path. It does not mention error behavior or cost of selecting 'all', but the read-only intent is reasonably clear from 'Hydrate' and the explicit caution against rewriting.

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?

The description is compact: two sentences, no redundant filler, and the most important behavioral caution is placed at the end. The include list is a little run-on, but every phrase earns its place.

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

Completeness3/5

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

For a tool with no output schema and only 50% parameter schema coverage, the description is helpful but not complete. It explains include options and the default, but it does not clarify what id refers to, whether id can be omitted, or what the returned hydration structure looks like.

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?

The description adds useful semantics to the include parameter by explaining the default 'summary' means counts and listing the meaningful sub-resource options. However, the id parameter is left entirely to inference, and with 50% schema description coverage, that is a real gap.

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 uses a concrete action ('Hydrate records') and lists the included sub-resources (dictionaries, parameters, observations, edges, all), which distinguishes it from targeted siblings like model_get_parameter or model_list_observations. The resource is slightly vague ('records' rather than 'the model register'), but the title and include list clarify the intended scope.

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?

The description explicitly names model_upsert_observation as the alternative for changing a single fact and warns against rewriting the hydrated blob. However, it does not contrast this tool with the various model_list_* getters, so an agent must infer when the broad hydration path is preferable to a narrower list call.

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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TDQS

B3.2/5.0
Disambiguation2/5

Many tools are mirrored across `artifacts_*` and `roadflow_*` with near-identical names and behavior, and within each family `get`, `export_json`, and `export_markup` overlap in what they return. Descriptions identify the target workspace, but an agent must carefully inspect prefixes and formats to avoid misselection.

Naming Consistency4/5

The set consistently uses lowercase snake_case with a domain prefix and predictable verbs like get, list, create, open, export, and apply. Minor deviations are bare commands (`new`, `discard`, `status`) and the parallel `artifacts_*`/`roadflow_*` prefixes, which make names look duplicated.

Tool Count3/5

At 23 tools, the surface lands in the heavy 16-25 range and feels padded because many operations are duplicated for two workspace types. Each subsystem alone would have a reasonable count, but combined the set is bloated.

Completeness4/5

The toolset covers the full workspace lifecycle: create, read, update via apply, discard, share, version, status, and multiple export formats. Missing cloud deletion and fine-grained element editing are minor gaps that can be worked around with full-state apply/export.

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