Server Quality Checklist

Tool Scores

• sequential_consultation_chainB
  3/5.0
  Behavior2/5

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

  The description hints at sequential dependency but fails to disclose error handling, memory effects, or resource usage. With no annotations, this leaves significant behavioral gaps.

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

  Conciseness3/5

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

  The description is a single concise sentence, but it lacks structure and does not earn its place for the tool's complexity.

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

  Completeness1/5

  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, 4 params, no output schema, no annotations), the description is far too minimal and incomplete.

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

  Parameters1/5

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

  Schema coverage is 0% and the description does not explain any of the 4 parameters (consultants, context, flowControl, memory), providing no meaning beyond the schema structure.

  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 runs a sequence of consultations with chaining, which distinguishes it from siblings like consult_ollama (single) and compare_ollama_responses (comparison). It uses specific verbs and explains multi-step reasoning.

  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 implies use for complex multi-step reasoning, differentiating from single consultations, but lacks explicit when-not-to-use or direct comparisons to sibling tools.

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

• remember_contextB
  3.3/5.0
  Behavior2/5

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

  No annotations are provided, so the description must disclose behavioral traits. It only states 'store context' without specifying overwrite behavior, persistence scope, size limits, or side effects, which is insufficient for a mutation operation.

  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 sentence that is efficient and front-loaded. While it is concise, it could incorporate more detail without becoming verbose.

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

  Completeness2/5

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

  Given no output schema and the presence of sibling tools, the description is too minimal. It lacks details on return values, error handling, session boundaries, and how the stored context interacts with other tools.

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

  Parameters3/5

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

  The input schema provides 100% coverage with descriptions for all three parameters (key, value, metadata). The description adds no additional meaning beyond the schema, so it meets the baseline of 3.

  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 action ('store'), resource ('context'), and purpose ('for use in future consultations'). It differentiates from sibling tools like 'compare_ollama_responses' and 'consult_ollama' by focusing on memory/retention.

  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 implies usage within a session but provides no explicit guidance on when to use versus alternatives (e.g., sequential_consultation_chain). No exclusions or prerequisites are mentioned.

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

• compare_ollama_responsesB
  3.4/5.0
  Behavior2/5

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

  No annotations are provided, so the description must disclose behavioral traits. It only states a high-level purpose without details on behavior, such as default model selection, response format, error handling, or any side effects. This is insufficient for an agent to predict tool behavior.

  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 a single sentence that is front-loaded with the core purpose. It is concise with no unnecessary words or repetition.

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

  Completeness2/5

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

  Despite having 3 parameters and no output schema, the description provides only a minimal purpose. It fails to explain how the tool works (e.g., default model behavior, output structure), leaving significant gaps for an agent to correctly invoke the tool. The sibling tools suggest more specialized alternatives, but no cross-referencing is provided.

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

  Parameters3/5

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

  The input schema has 100% description coverage for all three parameters. The description does not add new meaning beyond what is already in the schema, so the baseline score of 3 applies. The description's mention of 'multiple models' aligns with the models parameter but does not enhance it.

  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 compares responses from multiple Ollama models on the same prompt, with the goal of diverse perspectives. This specific verb+resource combination distinguishes it from the sibling tool consult_ollama, which is for single model queries.

  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 implies usage for getting diverse perspectives, but provides no explicit guidance on when to use or not use this tool versus alternatives like consult_ollama or sequential_consultation_chain. No exclusions or prerequisites are mentioned.

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

• consult_ollamaB
  3.4/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It discloses support for sequential chaining and model selection logic, but lacks information on side effects, idempotency, authentication requirements, or rate limits. The behavioral traits are partially covered.

  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 concise at two sentences, covering purpose and a key feature (chaining). It is front-loaded with the primary use. However, it lacks structure such as bullet points or explicit sections that could improve scannability.

  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 complexity (8 parameters, nested objects, no output schema), the description offers adequate context about usage and chaining. However, it does not describe return values, error conditions, or the typical response format, leaving a completeness gap.

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

  Parameters3/5

  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 minimal value beyond the schema, only tying together the chaining context for the 'context.previous_results' parameter. Most parameter meaning is already clear from the schema itself.

  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 specifies the tool's purpose: consulting with Ollama AI models for architectural decisions, code reviews, and design discussions. It includes mention of sequential chaining, which helps differentiate from the sibling tool 'list_ollama_models' but could be confused with 'sequential_consultation_chain'.

  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 implies usage for reasoning tasks but does not explicitly state when to use this tool versus alternatives like 'compare_ollama_responses' or 'sequential_consultation_chain'. No exclusions or when-not-to-use guidance is provided.

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

• list_ollama_modelsA
  4.3/5.0
  Behavior4/5

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

  With no annotations, the description carries the behavioral burden. It indicates the tool lists models from local system (installed and cloud-based), which is a safe read operation. However, it does not explain how the list is fetched or any potential latency.

  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 a single clear sentence with no redundancy. It is appropriately sized and front-loaded.

  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 zero parameters, no output schema, and no annotations, the description fully explains what the tool does and its scope. Sibling tools cover different operations, making this complete.

  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?

  No parameters exist, so the baseline is 4. The description adds meaning beyond schema by specifying the scope (local system, installed or cloud-based).

  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 lists all available Ollama models on the local system, including both installed and cloud-based. It is distinct from sibling tools like compare_ollama_responses or consult_ollama.

  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 implies usage for retrieving available models but provides no guidance on when to use versus alternatives or when not to use. No explicit context or exclusions.

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