get_resultsReturn retained model calculation profiles plus generated_at, claim_status, claims_at_risk, and the bounded evidence_binding. These rows are not durable named-model performance claims. Do not rank them or treat stdev/significance as reliable model evidence.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Beyond annotations (readOnlyHint=true, openWorldHint=false), the description explains that rows are not durable named-model performance claims and warns against misusing statistical measures. This adds valuable context about data limitations, though not all behavioral aspects are covered.
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?
The description is two sentences, front-loaded with the action, and every sentence adds essential information without redundancy. No unnecessary words.
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 no input parameters and no output schema, the description is adequately complete. It explains what is returned and provides critical usage warnings. Minor lack of detail on return format but sufficient for the tool's simplicity.
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 tool has no parameters, so the description is not required to add param details. Baseline score of 4 is appropriate as the description does not need to compensate for missing schema info.
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 explicitly states the verb 'Return' and the resource 'retained model calculation profiles', listing specific fields. This clearly defines the tool's action and output, distinguishing it from siblings like 'compare_models' or 'get_case'.
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 provides clear guidance on appropriate use: returning calculation profiles without treating them as durable model evidence. It warns against ranking and misinterpreting stdev/significance. However, it does not explicitly contrast with siblings like 'compare_models' for when to prefer this tool.
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 has a distinct purpose: compare_models compares two profiles, get_case retrieves a full case, get_methodology explains the benchmark design, get_results returns calculation profiles, and list_cases lists metadata. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern (compare_models, get_case, get_methodology, get_results, list_cases) using underscores and imperative verbs.
5 tools is well-scoped for a benchmark analysis server. Each tool covers a necessary function without being excessive or lacking.
The tool set covers the full lifecycle of exploring the benchmark: listing cases, retrieving details, understanding methodology, accessing results, and comparing models. No obvious gaps given the read-only purpose.