snapshotCapture an immutable point-in-time snapshot of a model's state (base document + derived/meta caches). A natural safety step before a risky evolve_spec: keep the returned snapshot and, if the change goes wrong, hand it back to 'restore' to roll the state back.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The model id. |
| 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 supplied readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context by noting the snapshot is immutable, point-in-time, and covers derived/meta caches, plus how the returned object should be used for rollback.
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 fluff; the core action is front-loaded and the safety workflow is compactly explained. Every sentence adds distinct value.
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 one-parameter tool with an output schema and fully declared annotations, the description covers what it does, what it includes, when to do it, and what to do with the result. No crucial behavior is left unexplained.
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 has 100% description coverage for its single required parameter, correctly describing it as 'The model id', so the baseline of 3 applies. The tool description itself adds no extra detail about the id parameter, but none is strongly needed for this one-parameter API.
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?
States a specific verb and resource: 'Capture an immutable point-in-time snapshot of a model's state' including the exact contents (base document + derived/meta caches). It is clearly differentiated from the rollback workflow via restore, though it does not explicitly contrast with the sibling get_state tool.
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?
Provides explicit guidance: 'A natural safety step before a risky evolve_spec' and connects the returned snapshot to the restore operation. It lacks a full when-not-to-use list, but the primary use case and recovery workflow are unambiguous.
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.