Server Quality Checklist

Tool Scores

• solve_constraint_modelA
  4.6/5.0
  Behavior4/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations, the description fully discloses the tool's behavior: it supports discrete optimization with integer/bool variables, multiple constraint types, and linear objectives. It explains return statuses and provides tips for interpretation. It does not explicitly state whether the tool is read-only or has side effects, but the nature of solving implies no state mutation.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is well-organized with clear sections (intro, use cases, args, returns, tips, example). Every sentence adds value, no redundancy. Despite length, it is front-loaded with purpose and efficient.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (5 parameters, nested objects, no output schema), the description covers all input details, return fields, and provides practical tips. It is sufficient for an agent to use the tool correctly without external references.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 0%, yet the description completely explains all parameters, including nested structures (variables, constraints, objective, search). Each constraint kind has detailed param specs, and an example illustrates usage. This fully compensates for the lack of schema descriptions.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description states 'Solve a general constraint or optimization model' and lists diverse use cases (scheduling, puzzles, assignment, routing). This clearly differentiates it from sibling tools that target specific problems like scheduling or routing, establishing a clear general-purpose scope.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides explicit guidance on when to use 'satisfy' vs 'optimize' modes, tips for diagnosing infeasibility, and examples. However, it does not explicitly mention when to prefer sibling specialized tools (e.g., solve_scheduling_problem) over this general solver, leaving the decision to the agent.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• solve_scheduling_problemA
  4.8/5.0
  Behavior5/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations provided, but the description fully explains behavior: automatically builds CP-SAT model, returns detailed response with status, makespan, schedule, resource utilization, critical path, solve time, optimality gap, and human-readable explanation. Warns about infeasibility and circular dependencies.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Description is fairly long but well-structured with sections for description, arguments, returns, tips, and examples. Every part adds value, but there is some redundancy (e.g., duplicate explanations in text and examples). Could be slightly more concise.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given 4 parameters and no output schema, the description is comprehensive: explains input structure, returns, tips for LLMs, and includes two examples covering basic and resource-constrained scenarios. Covers error handling and edge cases like circular dependencies.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 0% per context, so description must compensate. The description does not add much to individual parameter definitions beyond the schema, but it provides extensive usage guidance and examples that show how to use parameters effectively. The tips section adds value for an AI agent.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states it 'Solve a task scheduling problem with dependencies and resource constraints'. It distinguishes from sibling 'solve_constraint_model' by specifying this is for tasks with durations, dependencies, and resource constraints.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Explicitly states 'Use this instead of solve_constraint_model when you have tasks with durations, dependencies, and resource constraints'. Provides tips for LLMs on extracting task information and checking for circular dependencies. Examples illustrate typical use cases.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• solve_assignment_problemA
  4.9/5.0
  Behavior5/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations, the description fully discloses behavioral traits: it describes the optimization behavior (minimize_cost, maximize_assignments, balance_load), hard constraints from skills, forced assignment behavior, default cost matrix logic, solver timeout, and return values. It also explains that skills create hard constraints and provides agent load distribution details.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is well-structured with clear sections (purpose, usage, args, returns, tips, examples). While lengthy, every sentence adds value and the information is front-loaded. It avoids redundancy but could be slightly more concise without losing effectiveness.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  The description is complete for a complex tool: it covers parameter semantics, behavioral constraints, return values (including a structured response object), usage scenarios, and example code. No output schema exists, but the description compensates by listing response fields. It addresses potential edge cases (force_assign_all, unassigned tasks) and provides LLM tips.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Despite the schema description coverage being 0%, the description provides detailed explanations for all 6 parameters: agents, tasks, cost_matrix, objective, force_assign_all, max_time_ms. It includes default values, structure of nested objects, and examples that clarify usage. This adds significant meaning beyond the schema items' basic descriptions.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states 'Solve a task assignment problem' and distinguishes from sibling tools by specifying 'Use this instead of solve_constraint_model when you need to assign tasks to agents/workers with capacity and skill constraints.' It also mentions alternatives like solve_budget_allocation and solve_routing_problem implicitly through the sibling list.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides explicit guidance on when to use this tool ('Use this instead of solve_constraint_model'), and includes context for LLMs with tips on assignment vs matching, skill constraints, and objective selection. It also gives examples demonstrating different scenarios (simple assignment, with skills, balance load), reinforcing usage guidelines.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• solve_budget_allocationA
  4.9/5.0
  Behavior5/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations provided, so description carries full burden. It thoroughly explains the solver's behavior: handles dependencies, conflicts, multi-resource budgets, soft/hard constraints via penalty_per_unit_over, and returns a detailed response with status, costs, etc.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Well-structured with clear sections (intro, Args, Returns, Tips, Examples). Slightly verbose but every section adds value; could be trimmed slightly without losing clarity.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (8 parameters, nested structures, no output schema), the description covers all aspects: parameter details, return fields, multiple examples, and usage tips. Exceptional completeness.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The 'Args' section adds significant meaning beyond the schema, explaining item structure, budgets, objective, thresholds, and solver time. Examples illustrate typical usage patterns.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool solves budget allocation/knapsack problems and distinguishes it from the sibling solve_constraint_model, stating 'Use this instead ... when you need to select items under budget constraints with dependencies and conflicts.'

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Provides explicit guidance on when to use this tool (instead of solve_constraint_model) and gives tips for various applications (portfolio, features, campaigns) and objective choices (maximize_value vs maximize_count).

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• solve_routing_problemA
  4.9/5.0
  Behavior5/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations, the description fully discloses return values, status codes (OPTIMAL, FEASIBLE, INFEASIBLE), solver time limit, route structure, and behavior like returning to start. No negative side effects mentioned, which is appropriate for a solver tool.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is lengthy but well-structured with sections, bullet points, and examples. It is front-loaded with purpose and each section earns its place. Slightly verbose due to comprehensive examples, which is justified by the tool's complexity.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (5 parameters, nested objects, no output schema), the description covers all input semantics, return values, and usage scenarios with examples. It is fully adequate for an agent to understand and invoke the tool correctly.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 0%, but the description provides detailed parameter documentation including types, defaults, and usage tips. It adds meaning beyond the schema, such as explaining the 'coordinates' tuple format and 'capacity' default unlimited.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  Clearly states it solves vehicle routing problems (TSP/VRP) with optimal route planning. Distinguishes itself from siblings like solve_scheduling_problem by focusing on routing, not scheduling or assignment.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Provides explicit guidance on when to use TSP vs VRP, capacity constraints, distance_matrix vs coordinates, and different objective options. Includes tips and examples for common use cases, making it easy for the agent to choose correctly.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.