get_viewEvaluate a model's embedded view definition against current state and return the resolved component tree. Pass an optional 'viewId' for a named view; omit for the default view.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The model id. | |
| viewId | No | Optional named view id; omit for the default view. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context: it evaluates against 'current state' and returns a 'resolved component tree,' which clarifies what the tool actually computes beyond the annotation hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler: the first states the core action and result, and the second gives the only parameter-level decision the caller needs. Information is front-loaded and every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter, read-only, idempotent tool with an output schema, the description is complete enough to support correct invocation. It covers required id, optional viewId, default behavior, and the returned component-tree outcome; annotations cover side-effect safety.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description's note about omitting viewId for the default view adds slight semantic emphasis, but it largely restates the schema and does not meaningfully expand parameter meaning beyond it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific action ('Evaluate') and resource ('a model's embedded view definition'), with a distinctive outcome: 'resolved component tree.' It is clear, but it does not explicitly distinguish itself from sibling get_* tools such as get_spec or get_state, so it misses the full sibling-differentiation bar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys when the tool is relevant: when you need a model's view definition evaluated against current state. However, it offers no explicit guidance about alternatives or exclusions among the many sibling tools, so the usage context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, but get_audit explicitly subsumes get_history and explain, and get_state with paths overlaps get_field, creating minor selection ambiguity. The detailed descriptions help, but an agent could still reach for the wrong getter.
Naming is overwhelmingly consistent: snake_case with verb_noun structure and coherent get_/create_/delete_ clusters. Minor deviations like bare verbs (mutate, explain, restore, snapshot) and eval instead of evaluate prevent a perfect score.
27 tools is above the comfortable range and feels heavy, especially with several overlapping audit/state getters that could be consolidated. That said, the domain is broad enough that the count is defensible, so it is heavy but not chaotic.
The tool set covers the full model lifecycle well: create, validate, test, mutate, evolve, read, delete, plus snapshot/restore, audit, blobs, views, library, and expression evaluation. Minor gaps like explicit export/import or separate view-management tools are workable around.