search_typesSearch for types across ALL companion specs by keyword. Use this when you don't know which namespace defines a type. Returns matching types with their namespace alias.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search keyword (e.g. "identification", "temperature", "motion") |
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 signal readOnly and idempotent behavior, and the description adds the important cross-spec search scope plus the fact that results include the namespace alias for disambiguation. It does not describe match semantics like case-sensitivity or substring matching, but this is minor for a read-only search.
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?
Three short sentences, each carrying distinct information: the action, the trigger condition, and the return shape. There is no filler or redundancy.
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?
With one required parameter, no output schema, and annotations covering safety, the description is sufficient: it states scope, expected output, and when to use it. Nothing is missing that would prevent a correct invocation.
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?
The single query parameter is fully described in the schema, including example keywords, so the schema does the heavy lifting. The description adds little beyond calling it a keyword, which is already present in the schema.
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 names a specific action ('search for types') and a defined scope ('across ALL companion specs'), which clearly differentiates it from siblings like list_types or get_type_details. The immediate usage condition ('when you don't know which namespace defines a type') further pins down its role.
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?
Explicitly states the trigger condition: use this when the namespace is unknown. It stops short of naming sibling alternatives or stating when not to use it, such as when the namespace is already known, which prevents a perfect score.
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.
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.