Server Quality Checklist

Tool Scores

• capabilitiesA
  4/5.0
  Behavior3/5

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

  Annotations already provide readOnlyHint=true and idempotentHint=true. Description adds 'bounds it enforces' hinting at limits but no detail. Adequate given annotations.

  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?

  Extremely concise, purpose stated upfront in first sentence, no filler. Efficient for an introspection tool.

  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?

  Output schema exists but not shown; description covers basic purpose. Could elaborate on 'bounds' but sufficient for a simple capability listing.

  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?

  With 0% schema coverage, description adds value by explaining include_models parameter ('Include the full substitution-model list (long)') beyond the schema's title and default.

  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?

  Description clearly states it returns server capabilities and bounds, which is specific and distinguishes from sibling tools like infer_tree or select_substitution_model that perform different actions.

  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?

  No explicit when-to-use or alternatives, but the introspective nature is implied. Could benefit from noting it's for discovery before using other tools.

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

• compare_treesA
  4.3/5.0
  Behavior3/5

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

  Annotations already declare readOnlyHint and idempotentHint true, so the description adds only the nuance that it compares splits. No contradictions, but minimal extra behavioral disclosure.

  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?

  Three concise lines plus Args, no redundancy. Every sentence adds value; front-loaded with purpose.

  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?

  Tool is simple with two params and has an output schema. Description covers concept, input format, and key distinction (split vs string). Complete for the complexity.

  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 has 0% description coverage, but the description's Args section adds meaning: 'First tree in Newick format' and 'Second tree in Newick format'. This compensates well, though not exhaustive.

  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 computes Robinson-Foulds distance and identifies differing clades, distinguishing it from string comparison. Specific verb+resource with unique focus.

  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?

  Explicitly compares splits not strings, hinting that string equality is a different task. Sibling tools like infer_tree are distinct, so no confusion. Slightly lacking explicit when-not or alternatives.

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

• select_substitution_modelA
  4.6/5.0
  Behavior4/5

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

  Given that annotations already declare readOnlyHint and idempotentHint, the description adds useful behavioral context: it explains that the output includes the ranking, delta to the next model, and whether AIC/AICc/BIC agree, and that the seed fixes the engine's search. This goes beyond the annotations but is not exhaustive (e.g., no mention of error handling or performance), making a 4 appropriate.

  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 appropriately sized with a clear opening statement followed by a concise, well-organized Args list. Every sentence adds value: the first sentence states the purpose, the second explains what makes the output meaningful, and the Args section describes parameters without verbosity. It is front-loaded 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?

  The tool has an output schema, so the description does not need to detail return values, yet it still provides key context about the output (ranking, delta, AIC/BIC agreement). With 5 parameters and one required, the description covers all necessary aspects including sequence_type comparability and the role of the seed. It is complete for an AI agent to select and invoke 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?

  The schema has zero description coverage (0%), so the description must fully compensate. It does this effectively by listing every parameter and adding meaning: it explains what fasta is, defines the criterion values with a note about BIC, states that seed fixes the search, clarifies top_n as the number of ranked models, and elaborates on sequence_type with the comparability caveat. This fully covers the schema's gaps.

  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 opens with a specific verb phrase, 'Compare substitution models and report how much the winner won by,' which clearly states the tool's function and output. It distinguishes itself from sibling tools like infer_tree and compare_trees by focusing on substitution model ranking rather than tree inference or comparison.

  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 clear context for when to use the tool (when comparing substitution models) and includes a usage-relevant caveat: nucleotide and protein models are not comparable, so sequence_type must be chosen appropriately. However, it does not explicitly mention alternative tools or define when NOT to use this tool, so it scores a 4 rather than a 5.

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

• simulate_alignmentA
  4.6/5.0
  Behavior4/5

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

  Annotations already declare idempotentHint=true and readOnlyHint=true, and the description adds context about being a simulation with known truth, without contradicting annotations.

  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?

  Concise at about 6 sentences, front-loaded with purpose, but the Args listing as a paragraph could be more structured; still clear and efficient.

  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 presence of an output schema, the description fully covers usage, parameters, and purpose. It does not mention the exact output format but that is handled by the schema.

  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 0% schema coverage, the description explains each parameter (newick, model, length, seed) with meaningful context beyond name/type, such as 'with branch lengths' for newick and 'fixes the simulation' for seed.

  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 'generate sequences along a tree you specify' with a specific verb and resource, and distinguishes from siblings like infer_tree and compare_trees by framing it as a positive control.

  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 positions this tool as a positive control, suggests a workflow with infer_tree and compare_trees, and explains how to interpret results if inference fails.

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

• infer_treeA
  5/5.0
  Behavior5/5

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

  The description adds substantial behavioral detail beyond the annotations (readOnlyHint=true, idempotentHint=true): it always bootstraps with no option to skip, cost scales linearly with replicates, seed fixes both resampling and engine search, and sequence_type is 'DECLARED, never sniffed' to avoid silent model mismatches. These are not captured by the annotations and provide valuable transparency.

  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 front-loaded with the core purpose, then uses a clear Args list for parameter details. Every sentence earns its place—even the 'DECLARED, never sniffed' note is relevant. It is appropriately sized for a tool with 5 parameters and nuanced bioinformatics caveats, with no filler.

  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 and that an output schema exists (so return values need no explanation), the description covers all critical context: prerequisites (aligned sequences), model selection guidance, bootstrap behavior, cost implications, and cross-tool dependencies (select_substitution_model). It is fully sufficient for an agent to understand when and how to invoke the tool.

  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 compensates fully by explaining every parameter: fasta must be aligned and same-length, model examples and recommendation, replicates range and cost, seed's dual role, and sequence_type with a warning about protein alignments needing protein models. This is essential meaning that the schema alone lacks.

  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 opens with 'Build a maximum-likelihood tree and measure how well the data support it,' which is a specific verb+resource statement that clearly differs from sibling tools like select_substitution_model (model selection) and compare_trees (tree comparison). The title reinforces the purpose, and the description explicitly notes the tool always bootstraps, making it the right choice for supported phylogenetic inference.

  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 when-to-use guidance: 'Run `select_model` first if you do not have a reason to prefer one' and 'run select_substitution_model with the same sequence_type first' for protein data. It also states a key prerequisite: 'this server does not align,' implying the tool should not be used with unaligned sequences. These are clear usage directives and alternatives.

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