recommend_modelRecommend models for a workload using price, context, and benchmark evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | limit | |
| budget | No | budget | |
| provider | No | provider | |
| workload | No | workload | |
| min_context_tokens | No | min context tokens |
| 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?
Annotations already indicate readOnlyHint=true, and the description is consistent. The description adds that it uses price, context, and benchmark evidence, which is slightly beyond the annotation but does not provide deep behavioral insight.
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?
One sentence, no wasted words. Front-loaded with the core purpose.
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 an output schema present and 100% schema coverage, the description is somewhat complete but could be improved by explaining how recommendations are generated and what the output contains.
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 coverage is 100%, but param descriptions are just names (e.g., 'limit'). The description adds some context by mentioning workload, price (budget), and context (min_context_tokens), but does not fully explain all parameters like provider.
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 the tool's purpose: recommending models based on workload, price, context, and benchmarks. It distinguishes from siblings like benchmark_lookup and calculate_cost, but could be more specific about what 'context' and 'price' refer to.
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?
No guidance on when to use or not use this tool versus siblings like compare_models or find_best_value. No prerequisites or exclusions are mentioned.
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 operation: searching, comparing, costing, benchmarking, history, news, etc. No two tools have overlapping purposes; descriptions clearly differentiate them.
All tool names follow a consistent verb_noun or verb_phrase pattern using underscore_case (e.g., search_models, find_cheapest, provider_summary). No mixing of styles or vague verbs.
11 tools is an appropriate scope for an AI pricing hub. Each tool serves a specific need without redundancy, and the count feels neither sparse nor bloated.
The tool set covers the full workflow: searching, comparing, cost estimation, benchmarking, historical data, provider overview, recommendations, and news. No obvious gaps for a read-only pricing resource.