Search theoretical models by keyword over name/summary/constructs. Optional filters: invokes_construct_id (theories that invoke a specific construct), field_domain (classification axis).
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | ||
| limit | No | ||
| field_domain | No | ||
| invokes_construct_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
| total | Yes | ||
| matches | Yes |
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 mark the tool as read-only and non-destructive. The description adds meaningful behavioral detail by specifying the searched fields and explaining both optional filters: invokes_construct_id for construct-invoking theories and field_domain as the classification axis. No contradiction with annotations.
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 concise, front-loaded sentences: the core action comes first, followed by optional filters. There is no redundant phrasing or filler.
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?
Given the annotations, output schema, and simple parameter set, the description covers what is searched and what the filters mean. The only notable gap is the absence of explicit guidance on when to choose this tool over sibling search/list/lookup variants.
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 0%, so the description carries the semantic load. It explains q as keyword over name/summary/constructs and glosses invokes_construct_id and field_domain. Only limit is left to the schema's numeric constraints, which are simple enough.
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 clearly states a verb and resource: keyword search over theoretical models, scoped to name/summary/constructs. It is unambiguous about what is searched, though it does not explicitly distinguish itself from sibling search tools like principia.models.search.
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 gives a clear usage context: use it for keyword search over theoretical models with optional filters. It does not, however, explain when to prefer this tool over the sibling list or lookup tools, nor does it name alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.