get_fieldGet the value of a single field by its JSON Path address (e.g. "$.order.total"). Evaluates a LAZY derivation on demand.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The model id. | |
| path | Yes | A canonical JSON Path address, e.g. "$.order.total". |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The note 'Evaluates a LAZY derivation on demand' is the standout contribution — it warns the agent that reading the field may trigger extra computation, information not present in the annotations. The annotations already convey the safe idempotent read profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), and the description adds behavioral context on top.
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 efficient sentences with zero waste: the first declares purpose with a concrete example, and the second highlights the key behavioral trait. Every word 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 lookup with no output schema, this is complete. The annotations carry the safety profile, the description covers purpose and the lazy-evaluation nuance, and the schema documents the parameters — nothing an agent needs to call this correctly is missing.
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 coverage is 100%, so the schema fully documents both required parameters. The description reinforces the path parameter semantics with a concrete example but adds no additional parameter-level details beyond what the schema already provides.
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 states a specific verb (Get), a precise resource (the value of a single field), and the addressing mechanism (JSON Path, e.g. "$.order.total"). The qualifier "single field" plus the JSON Path example clearly differentiates this from sibling tools like get_state, get_spec, and get_view, which have broader scopes.
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 makes the use case clear: you use this when you need one field's value, addressed by a canonical JSON Path. However, it doesn't explicitly name any sibling alternatives or state when not to use it, leaving the routing inference implicit.
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.