Calculate estimated cost and latency savings from optimizations
| Name | Required | Description | Default |
|---|---|---|---|
| current_calls | Yes | Current number of API calls | |
| avg_latency_ms | No | Average latency per call in ms | |
| optimized_calls | Yes | Optimized number of calls | |
| cost_per_call_usd | No | Cost per API call in USD |
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?
No annotations are provided, and the description does not disclose any behavioral traits such as read-only nature, side effects, required permissions, or output format. The description only states the calculation purpose, leaving the agent without information about what the tool does beyond the basic function.
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 a single, concise sentence with no unnecessary words. It is appropriately sized for a simple calculation tool and gets straight to the point.
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 straightforward calculation tool with no output schema, the description covers the core purpose. However, it omits details like the output format, assumptions (e.g., default cost per call), and any edge cases. It is minimally complete but not rich.
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 full descriptions for all four parameters (100% coverage), so the baseline is 3. The description adds no additional parameter semantics beyond what the schema already offers, but it doesn't need to compensate since coverage is complete.
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 tool calculates estimated cost and latency savings from optimizations, which is a specific verb and resource. It clearly differentiates from sibling tools like analyze_flow or detect_redundancy by focusing on savings calculation.
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 usage is implied by the context of the sibling tools (e.g., after detecting redundancy or suggesting batching), but there is no explicit guidance on when to use this tool versus alternatives. It lacks a clear when-to-use or when-not-to-use statement.
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 aspect of API flow analysis: overall flow, savings, redundancy, dependency graph, batching, and individual call tracking. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun pattern using snake_case, making the set predictable and easy to navigate.
Six tools is well-scoped for an API flow analyzer, covering core diagnostics without unnecessary bloat or missing essentials.
The surface covers tracking, analysis, redundancy detection, batching, dependency graphing, and savings calculation. Minor gaps like cleanup or export are absent but not critical for the domain.