Server Quality Checklist

Tool Scores

• local_modelsA
  4.2/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden. It discloses that the tool lists models on disk and indicates which is used by default, which is genuinely useful behavioral context. However, it doesn't mention performance, whether it hits the network, or the structure of the returned listing. For a read-only list operation, this is adequate but not rich.

  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 two sentences with zero wasted words. It front-loads the core purpose and then adds a concrete usage trigger. Every sentence earns its place.

  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?

  For a zero-parameter read-only listing tool with no output schema, the description covers purpose, what the output contains (sizes, default), and when to use it. It could add a bit more about the returned data format or fields, but the description is largely sufficient for this simple tool type.

  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 tool has 0 parameters and 100% schema coverage, so the baseline per the rubric is 4. The description adds meaning about what the output provides (sizes, default model), orients the agent on what information is returned, and there are no parameters requiring explanation.

  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 what the tool does: list models on disk with sizes and the server's default model. It uses a specific verb ('List') with a clear resource ('local models') and distinct output details. It also distinguishes its purpose from siblings through the 'default' model note and the usage context about checking model existence.

  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 gives an explicit usage context: 'Call it when a request names a model you are not sure exists.' This tells the agent when to invoke it. It doesn't explicitly name alternatives or exclusions, but the sibling tools (local_ask, local_classify) serve clearly different purposes, so the available guidance is reasonably complete for this simple listing tool.

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

• local_classifyA
  4.8/5.0
  Behavior5/5

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

  No annotations are provided, so the description carries the full burden — and it delivers richly. It discloses that output is validated against label set, returns matched=false with raw reply on hedging, can return declined=true, and honestly warns that a small model will still pick a confident in-set label for out-of-set text ('triage, not a verdict'). This is unusually candid behavioral disclosure beyond what any structured field could convey.

  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 a single focused paragraph that front-loads the core behavior then layers in caveats and use cases. Every sentence earns its place, but it's slightly dense and could be split into short sections for scannability. Efficient and non-redundant despite its length.

  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?

  This is a moderately complex tool (5 params, nuanced behavior around declined/matched and model error modes). With 100% schema coverage, no output schema, and no annotations, the description fully compensates: it explains validation behavior, edge cases (hedging, no-label), trust limitation, and appropriate use cases. The behavioral nuance of a model-based classifier is well covered.

  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 coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining the behavior behind allowNone (the declined escape hatch, forced-choice mislabelling risk) and clarifying that labels are the constraint the reply is validated against. It doesn't add detail on model or instruction params beyond the schema, but the schema already covers them. Marginal added value justifies a 4.

  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 uses a specific verb+resource pair ('classify text into labels you supply') and clearly distinguishes from siblings local_ask and local_models by framing it as triage/classification rather than open-ended chat. It even names concrete uses (log lines, error vs warning, which files look relevant), grounding the purpose.

  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 explicitly tells when to use it ('Good for triage'), frames its limits ('triage, not a verdict'), and gives concrete when-to-use examples. It advises on the allowNone flag ('Set allowNone=false only when a forced choice is genuinely wanted'). It's missing an explicit 'not for X, use sibling instead' but the triage framing plus sibling names imply exclusion well.

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

• local_askA
  4.9/5.0
  Behavior5/5

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

  With no annotations provided, the description carries the full burden and does an excellent job: it discloses the local/offline nature (nothing leaves the machine), no API cost, no file access, cannot run anything, roughly 16 tok/s for a 7B model, and that no conversation context is carried over. This is rich behavioral context that goes well beyond any schema.

  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 dense but efficiently structured: it opens with the core action, then capability boundaries, then usage examples, then constraints, then alternatives. Every sentence earns its place and there is zero filler or repetition.

  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 there is no output schema and no annotations, this description is remarkably complete: it covers purpose, constraints, performance characteristics, safety boundaries, and alternatives. For a parameter-rich tool (5 params) it fully compensates for the missing structured metadata.

  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 100%, so the baseline is 3. The description adds value by naming maxTokens as the main latency lever and relating it to the tok/s speed, which connects the parameter to a concrete performance behavior beyond mere syntax. It doesn't cover every parameter explicitly but the schema already handles most of that burden.

  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 exactly what the tool does: sends a prompt to a local model via Ollama and returns the answer immediately. It clearly identifies the resource (local model) and verb (send prompt / get answer), and differentiates it from siblings by noting it is the local-model option distinct from local_classify and local_models.

  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 when-to-use guidance ('summarising long output, drafting boilerplate or commit messages, extracting fields from text') and explicit when-not-to-use with named alternatives ('For work needing judgement, repo context, or edits on disk, do it yourself or use codex_start'). Also gives practical guidance on keeping outputs short and mentions the CPU speed constraint.

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