Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is known. The description adds that the output is ~500 words and covers specified topics, which supplements the structured data without contradiction.
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 entire description is two sentences: the first states the deliverable and content scope, the second gives usage guidance. No filler or redundant phrasings.
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 conceptual explainer with no inputs, the description covers purpose, output length, content areas, and when to use it. Since there is no output schema, the stated '~500-word educational explainer' adequately sets expectations.
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 has zero parameters and the description explicitly notes 'No inputs,' aligning with the 0-parameter baseline of 4. No additional parameter semantics are needed.
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 opens with 'Return a ~500-word educational explainer of M/M/c queueing theory' and enumerates specific topics (Little's Law, utilization, etc.), clearly distinguishing it from sibling tools that simulate or compare scenarios.
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?
It explicitly states 'Use this when the user asks a conceptual 'why' or 'how does this work' question' and contrasts with 'rather than asking for a number.' However, it does not name specific sibling tools as alternatives, so it stops one step short of full guidance.
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 clearly distinct purpose: simulation (simulate_mmc, simulate_scenario, simulate_schedule), comparison (compare_analytical_vs_simulated, compare_separate_vs_pooled), inverse analysis (recommend_staffing), interpretation (interpret_result), education (explain_queueing_theory, explain_advanced_patterns), and scenario management (list_scenarios, describe_scenario). No two tools overlap significantly, and nuanced differences are explicitly documented (e.g., when to use simulate_mmc vs simulate_scenario).
All tool names follow a consistent verb_noun pattern in snake_case: compare_*, describe_*, explain_*, interpret_*, list_*, recommend_*, simulate_*. Even compound names like compare_analytical_vs_simulated are clearly structured and match the pattern. There are no mixed conventions or vague verbs.
11 tools is well within the ideal 3-15 range and each tool earns its place. The set covers simulation, comparison, recommendation, interpretation, education, and scenario discovery without redundancy or bloat. The count feels right for a queueing theory teaching and simulation server.
The tool surface is complete for its stated domain: it offers multiple simulation modes (generic, preset, custom schedule), an inverse staffing finder, analytical-vs-simulation comparison, pooled-vs-separate comparison, interpretation, and educational explainers. There are no obvious dead ends—users can model, validate, understand, and optimize queueing scenarios. The intentional exclusion of advanced pattern simulation is addressed by explain_advanced_patterns pointing to ChiAha.
