Look up one CanonicalTheoreticalModel by id (e.g., theory.jd_r). Returns the theory's canonical constructs, canonical relations (with predicates), descendant per-study models, and foundational citations.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| note | No | ||
| found | Yes | ||
| theoretical_model | 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 establish that the tool is read-only and non-destructive. The description adds useful behavioral context by specifying what the lookup returns: canonical constructs, canonical relations with predicates, descendant per-study models, and foundational citations. This goes beyond annotation coverage without contradicting it.
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 sentences with no filler. The first sentence states the action and key parameter; the second compactly enumerates the return contents. Every sentence 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 lookup tool with an output schema and safety annotations already provided, the description is complete. The agent knows the ID format, the resource type, and what to expect in the response. No critical information 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?
The schema provides only 'id' with minLength 1, so the description meaningfully compensates by explaining the ID identifies a CanonicalTheoreticalModel and giving a real-world example ('theory.jd_r'). This is valuable guidance beyond the bare 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 uses a specific verb and resource ('Look up one CanonicalTheoreticalModel by id') and provides a concrete example ID. This clearly distinguishes it from list/search siblings, which operate over multiple models rather than fetching a single one.
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 phrase 'by id' gives clear context: this tool is appropriate when the agent already has a specific CanonicalTheoreticalModel identifier. It does not explicitly name alternatives or exclusion criteria, but the usage situation is unambiguous and sufficient for correct tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.