Server Quality Checklist

Tool Scores

• solve_constraint_satisfactionB
  3.1/5.0
  Behavior2/5

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

  No annotations are provided, so the description must carry the full burden. It mentions using the Z3 SMT solver and a timeout parameter, but does not disclose behavioral traits like side effects, error handling, or whether it modifies state. The description adds minimal behavioral context beyond basic usage.

  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 a summary, usage statement, parameter list, return description, and example. It is front-loaded with the core purpose. Minor verbosity in the 'ideal for' sentence could be trimmed, but overall it is efficient and clear.

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

  Completeness3/5

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

  The description includes a helpful example and parameter explanations, but lacks details on return value format (e.g., sat/unsat, variable values) and does not address edge cases or error behavior. Given the tool's complexity and absence of output schema and annotations, more context would be beneficial.

  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%, so the description compensates well by explaining each parameter in the 'Args' section and providing a concrete example. The explanation of 'variables' having 'name' and 'type' fields, and 'constraints' as expressions, adds meaning that the schema alone lacks.

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

  Purpose4/5

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

  The description clearly states the tool solves constraint satisfaction problems using the Z3 SMT solver, and mentions its applicability to logical reasoning and puzzles. However, it does not explicitly differentiate from sibling tools like solve_constraint_programming or solve_linear_programming, leaving some ambiguity about when to choose this specific solver.

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

  Usage Guidelines2/5

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

  The description says the tool is 'ideal for' certain problem types but provides no guidance on when not to use it or how it compares to sibling tools (e.g., constraint programming, convex optimization). Without explicit exclusions or alternatives, an AI agent lacks the context to choose correctly.

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

• solve_constraint_programmingA
  3.6/5.0
  Behavior2/5

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

  No annotations are provided, so the description must fully disclose behavioral traits. It mentions OR-Tools as the solver and defines parameters, but lacks details on side effects, authentication, error handling, or limitations. The return description is minimal ('variable values and feasibility status').

  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 a summary, Args, Returns, and an example. It is front-loaded with the core purpose. However, it is somewhat verbose; slight trimming could improve conciseness without losing clarity.

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

  Completeness4/5

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

  Given the tool's complexity (nested objects, no output schema, 5 params), the description covers inputs, outputs, and usage example. It explains the objective and parameters. However, it does not detail the constraint expression syntax, which may be unfamiliar to users.

  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%, so the description must compensate. It provides a detailed Args section explaining each parameter, including types and optional fields, plus a concrete example. This adds significant meaning beyond the raw schema, which only has titles and types.

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

  Purpose4/5

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

  The description clearly states the tool solves constraint programming problems using OR-Tools and lists typical use cases like combinatorial optimization and scheduling. However, it does not explicitly differentiate from the sibling tool 'solve_constraint_satisfaction', which could cause confusion.

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

  Usage Guidelines3/5

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

  The description indicates when the tool is ideal (e.g., combinatorial optimization, scheduling) but does not provide guidance on when not to use it or suggest alternative tools. Usage is implied from the domain listing, but no explicit exclusions or comparisons are given.

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

• solve_convex_optimizationA
  3.6/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden. It briefly mentions CVXPY but does not disclose behavioral traits such as side effects, authorization needs, or performance characteristics. The description is minimal in this regard.

  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 a one-liner, a clarifying paragraph, parameter documentation, return info, and an example. It is front-loaded and each part serves a purpose, though the example could be more integrated.

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

  Completeness3/5

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

  Given the absence of an output schema and annotations, the description covers input parameters well but falls short on return value details (only 'solution results including variable values and objective value'). The example helps, but more specifics on result structure are needed.

  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?

  The input schema has 0% description coverage, but the tool description includes a detailed parameter list with explanations (e.g., 'variables: List of variable definitions with name and shape') and an example. This adds significant meaning beyond the schema, though some details (e.g., shape format) could be clearer.

  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 that the tool solves convex optimization problems using CVXPY, and lists specific problem types (linear, quadratic, semidefinite programming). This distinguishes it from sibling tools like solve_constraint_satisfaction.

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

  Usage Guidelines3/5

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

  The description says it is 'ideal for convex optimization problems' which implies usage context, but it does not explicitly state when not to use it or mention alternative sibling tools. There is no direct guidance on choosing among similar tools.

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

• solve_portfolio_optimizationA
  4/5.0
  Behavior3/5

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

  No annotations are provided, so the description must bear the full burden. It describes the optimization goal (maximize return, constrain risk) and mentions returns, but lacks details on computational assumptions, error handling, or prerequisites (e.g., positive semidefinite correlation matrix).

  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-structured with a clear purpose statement, followed by an Args list, Returns, and Example. Every sentence adds value; no redundant information. It is concise yet comprehensive.

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

  Completeness4/5

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

  The description covers the tool's purpose, parameters, and returns. However, without an output schema, the return format is vague ('weights and performance metrics'). Missing details on error conditions or validation. Given complexity, it is mostly complete for basic usage.

  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%, so the description compensates well by listing each parameter with its meaning (e.g., 'List of expected returns', 'Optional maximum allocation limits') and providing a concrete example. It does not specify shape constraints or validation rules, but the example illustrates usage.

  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 solves portfolio optimization problems using modern portfolio theory (Markowitz mean-variance). The verb 'solve' and resource 'portfolio optimization' are specific and distinct from sibling tools like solve_convex_optimization or solve_linear_programming.

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

  Usage Guidelines3/5

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

  There is no explicit guidance on when to use this tool versus alternatives like solve_convex_optimization. The example implies usage for asset allocation, but no criteria for when not to use or what alternatives are available.

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

• solve_linear_programmingA
  4.3/5.0
  Behavior3/5

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

  No annotations provided, so description carries full burden. It describes solving behavior, variable types, and input format, but doesn't mention side effects, error handling, or performance characteristics. Adequate but not detailed.

  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: purpose sentence, ideal-use sentence, Args list, Returns line, and example. Slightly verbose due to extensive example, but example is highly valuable for understanding parameter usage.

  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 8 parameters (6 required), nested objects, and no output schema, the description covers all parameters with explanations, an example, and return description. It is complete and actionable for an agent.

  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 a detailed Args section with explanations for all parameters and a comprehensive example, adding significant meaning beyond the bare schema.

  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 linear and mixed-integer programming problems using HiGHS', specifying the exact type of problems (linear, MILP, large-scale with linear constraints). This differentiates it from siblings like solve_constraint_programming and solve_convex_optimization.

  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?

  It says 'ideal for linear programming, mixed-integer linear programming, and large-scale optimization problems with linear constraints', guiding when to use. However, it does not explicitly state when not to use or mention alternatives, though sibling names imply distinction.

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