resolve_dependenciesGiven one or more companion spec namespace aliases, returns the full resolved dependency list that MUST go in the YAML namespaces: section. Always call this before generating the namespaces block.
| Name | Required | Description | Default |
|---|---|---|---|
| aliases | Yes | Companion spec aliases to resolve (e.g. ["machineTool", "robotics"]) |
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?
The annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds that it produces a 'full resolved dependency list' and is a prerequisite step, but provides no details on return format, ordering, or failure behavior. This is adequate but not rich.
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 core behavior is stated first, and the workflow directive is front-loaded and directly actionable. Every clause 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 single-parameter read-only tool with clear annotations, the description covers the essential inputs, the output concept, and the workflow context. The absence of an output schema is mitigated by the statement that the result is a 'full resolved dependency list' for the YAML namespaces section.
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% and the schema already documents aliases as 'Companion spec aliases to resolve' with an example. The description's 'one or more' adds slight cardinality emphasis, but it mostly restates what the schema provides, so the baseline of 3 is appropriate.
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 uses a specific verb ('resolve'), a clear resource ('namespace dependencies'), and ties the result to a concrete artifact ('YAML namespaces: section'). This distinguishes it from siblings like list_namespaces by focusing on dependency resolution rather than mere listing.
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 explicit instruction 'Always call this before generating the namespaces block' tells the agent when to use the tool in the workflow. It does not mention alternatives or when not to use it, but the stated context is sufficient for correct sequencing.
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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Each tool targets a distinct resource or operation: unit lookup, reusable-block lookup, DSL reference, type details, namespaces, type listing/search, and the four model operations are all clearly separated. Even the two search-like tools (find_reusable_block and search_types) have different intents and return different kinds of results, so an agent should not misselect.
Most utility tools follow a verb_first_snake_case pattern (find_*, get_*, list_*, search_*, resolve_*), but the four workflow tools use an opcua_model_* prefix (create, generate, reverse, validate). This is a blend of two systematic conventions rather than chaotic naming, but it is not fully uniform.
Twelve tools is well within the ideal range for this domain, and each tool covers a distinct aspect of the modeling workflow from discovery and dependency resolution to validation and artifact generation. No tool feels redundant, and the count is appropriate for the stated purpose.
The tool surface covers the full lifecycle: discovering namespaces/types/units/reusable blocks, resolving dependencies, authoring YAML, validating it, generating artifacts, and reverse-engineering existing NodeSet files. There are no obvious dead ends or essential missing operations for the OPC UA modeling workflow.