Ollama MCP Server

by paolodalprato

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.9.1
Disambiguation4/5
Most tools have distinct purposes, but 'local_llm_chat' and 'select_chat_model' could cause confusion as they both relate to model selection for chatting, with some functional overlap. The other tools are clearly differentiated, such as health checks, model management, and resource monitoring.
Naming Consistency3/5
The naming is mixed, with some tools using snake_case (e.g., 'list_local_models', 'local_llm_chat') and others using more verbose or inconsistent patterns (e.g., 'ollama_health_check', 'system_resource_check'). While readable, there is no uniform verb_noun convention, and terms like 'check' and 'test' are used interchangeably.
Tool Count5/5
With 9 tools, the count is well-scoped for managing and interacting with Ollama models and servers. It covers installation, selection, testing, health, and resource checks without being overwhelming or too sparse, fitting the server's purpose effectively.
Completeness4/5
The tool set provides good coverage for model lifecycle (list, remove, test) and server management (health, start), with minor gaps such as updating models or handling advanced configurations. Core workflows like chatting and diagnostics are supported, allowing agents to perform most essential tasks.
Average 3.1/5 across 9 of 9 tools scored. Lowest: 2.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose whether this is read-only, what specific resources are checked (e.g., CPU, memory, disk), compatibility with what systems, or any side effects like performance impact. This leaves significant gaps in understanding the tool's behavior.
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, efficient phrase that is front-loaded with the core action. However, it's slightly under-specified, as it could benefit from a bit more detail without becoming verbose, but it avoids unnecessary words.
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 annotations, no output schema, and a simple input schema with 0 parameters, the description is incomplete. It doesn't explain what the check entails, what output to expect, or how it differs from sibling tools, making it inadequate for an agent to use effectively without additional context.
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 schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is acceptable given the lack of parameters, aligning with the baseline for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Check system resources and compatibility' restates the tool name 'system_resource_check' with minimal elaboration, making it tautological. It specifies the action ('check') and target ('system resources and compatibility'), but lacks detail on what resources or compatibility aspects are examined, and doesn't differentiate from siblings like 'ollama_health_check' or 'test_model_responsiveness'.
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?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., before model operations), or exclusions, leaving the agent to infer usage from the name alone among siblings like health checks or model testing tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal insight. It states the tool chats with a model but doesn't describe response format, error handling, rate limits, or whether it maintains conversation state. For a chat tool with zero annotation coverage, this is a significant gap in transparency about how the tool behaves beyond basic functionality.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information concisely.
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 the complexity of a chat tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (e.g., text response, structured data), error conditions, or dependencies like server status. For a tool that likely involves network calls and model interactions, more context is needed to guide effective use.
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 schema fully documents all three parameters (message, model, temperature) with their types, descriptions, and defaults. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate when the schema handles all parameter documentation.
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 action ('Chat with') and resource ('a local Ollama model'), making the purpose immediately understandable. It distinguishes from siblings like 'list_local_models' or 'remove_model' by focusing on interactive conversation rather than management tasks. However, it doesn't specify the exact scope (e.g., single-turn vs. multi-turn) or differentiate from 'select_chat_model' which might have overlapping functionality.
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?
No explicit guidance is provided on when to use this tool versus alternatives like 'select_chat_model' or 'test_model_responsiveness'. The description implies usage for general chat interactions but doesn't mention prerequisites (e.g., server running), exclusions, or comparative scenarios. This leaves the agent to infer context from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action is 'Remove' (implying destructive mutation) but lacks details on permissions needed, whether removal is reversible, error handling (e.g., if model doesn't exist), or side effects (e.g., impact on default settings). The force parameter hint in the schema suggests some behavioral nuance, but the description doesn't elaborate.
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, direct sentence with zero wasted words. It front-loads the core action and resource, making it highly efficient and easy to parse. Every word earns its place by conveying essential purpose.
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 the tool's destructive nature, lack of annotations, and no output schema, the description is insufficiently complete. It doesn't address critical context like what 'local storage' entails, confirmation requirements, success/failure indicators, or integration with sibling tools (e.g., checking models first with 'list_local_models'). For a mutation tool, this leaves significant gaps.
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 schema fully documents both parameters (model_name and force). The description adds no parameter-specific information beyond what's in the schema, such as format examples for model_name or implications of using force. This meets the baseline for high schema coverage.
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 action ('Remove') and target resource ('a model from local storage'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'list_local_models' or 'select_chat_model' beyond the obvious destructive nature, which prevents a perfect score.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a model to exist locally), exclusions (e.g., not for remote models), or relationships with siblings like 'list_local_models' for verification. This leaves the agent with minimal context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions presenting models and helping with selection, but fails to detail how this is done (e.g., interactive UI, list display, filtering criteria), what happens after selection (e.g., does it initiate chat automatically?), or any constraints like rate limits or permissions. This leaves significant gaps in understanding the tool's 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 extremely concise and front-loaded, consisting of a single, clear sentence: 'Present available models and help user select one for chat'. Every word contributes directly to the tool's purpose, with no wasted information or redundancy, making it efficient for quick comprehension.
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 the tool's complexity (involving model selection and chat initiation), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., selected model details, chat initiation status), behavioral nuances, or how it integrates with siblings like 'local_llm_chat'. For a tool with one parameter but potential behavioral depth, more context is needed to fully guide an AI agent.
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, with one parameter 'message' documented as 'The message the user wants to send after selecting a model'. The description adds no additional meaning beyond this schema, as it doesn't explain parameter usage or constraints. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description input.
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's purpose: 'Present available models and help user select one for chat'. It specifies the verb ('present' and 'help select') and resource ('models'), making the function understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_local_models' or 'suggest_models', which may have overlapping functionality.
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 provides minimal guidance on when to use this tool. It implies usage when a user needs to select a model for chat, but offers no explicit context on when to choose this over alternatives like 'list_local_models' or 'suggest_models', nor does it mention prerequisites or exclusions. This lack of comparative guidance reduces its effectiveness for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It states the tool tests responsiveness by sending a simple prompt, but lacks details on what 'responsiveness' entails (e.g., latency, availability, error handling), whether it's a read-only or mutative operation, or any side effects like rate limits or authentication needs. This leaves significant gaps in understanding the tool's 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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly, and every part of the sentence contributes essential information.
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 the lack of annotations and output schema, the description is incomplete for a tool that tests model responsiveness. It doesn't explain what 'responsiveness' means, what the simple prompt entails, or what the expected output or success criteria are. For a testing tool with no structured behavioral data, this leaves too many open questions about how to interpret results.
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, with the single parameter 'model_name' clearly documented. The description adds no additional meaning beyond the schema, such as examples or constraints on model names. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.
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 action ('Test the responsiveness') and target resource ('a specific model'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from potential alternatives like 'ollama_health_check' or 'system_resource_check' among the sibling tools, which might also test system or model status.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools like 'ollama_health_check' or 'local_llm_chat', leaving the agent to infer usage context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'suggests' models but doesn't describe what the suggestion looks like (e.g., ranked list, single recommendation), whether it requires model availability checks, or any performance characteristics. The description is minimal and doesn't provide adequate behavioral context for a tool with no annotation coverage.
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, efficient sentence that front-loads the core purpose. Every word earns its place: 'Suggests' (action), 'best locally installed model' (resource and scope), 'for a specific task' (context), 'based on user needs' (input basis). No wasted words or redundancy.
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?
For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the suggestion output looks like (e.g., model names, scores, explanations), doesn't mention potential errors (e.g., no models installed), and provides minimal behavioral context. Given the complexity of model selection and lack of structured output documentation, the description should do more.
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 schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema - it mentions 'user needs' and 'priority' but provides no extra context about format, examples, or constraints. Baseline 3 is appropriate when schema does all the work.
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's purpose: 'Suggests the best locally installed model for a specific task based on user needs.' It specifies the verb (suggests), resource (locally installed model), and scope (for a specific task). However, it doesn't explicitly differentiate from siblings like 'select_chat_model' or 'list_local_models' beyond the 'suggests' vs 'selects' or 'lists' distinction.
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 context ('for a specific task based on user needs') and scope ('locally installed'), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'select_chat_model' or 'list_local_models'. It mentions the 'priority' parameter which hints at trade-offs, but lacks clear when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It mentions the action ('start') and condition ('if it's not running'), but lacks details on behavioral traits such as what happens if the server is already running (e.g., error, no-op, success), required permissions, timeouts, or side effects. This is a significant gap for a tool that likely involves system operations.
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, efficient sentence that directly states the purpose and condition without any fluff. It is front-loaded and every word earns its place, making it highly concise and well-structured.
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 the complexity of starting a server (a system operation with potential side effects), no annotations, and no output schema, the description is incomplete. It lacks crucial details like what the tool returns (e.g., success/failure, server status), error handling, or dependencies, leaving significant gaps for an AI agent to use it effectively.
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 with 100% schema description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, and the baseline for 0 parameters is 4, as it avoids unnecessary details.
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 action ('start') and target resource ('Ollama server'), and includes the conditional 'if it's not running' which adds specificity. However, it doesn't explicitly differentiate from sibling tools like 'ollama_health_check' or 'system_resource_check' which might also interact with server status.
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 context ('if it's not running'), suggesting this tool should be used when the server is down. However, it doesn't provide explicit guidance on when to use alternatives like 'ollama_health_check' to check status first, or mention prerequisites or when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It states the tool performs a health check and provides diagnostics, but doesn't describe what specific diagnostics are included (e.g., server status, version, connectivity), whether it has side effects (e.g., pinging the server), or any rate limits or permissions required. For a diagnostic tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function ('Check Ollama server health and provide diagnostics'). It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple, parameterless tool. Every part of the sentence earns its place by conveying essential information.
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 tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on diagnostics content or behavioral traits. Without annotations or output schema, the description should ideally specify what 'health' and 'diagnostics' include to be more complete, but it meets the bare minimum for this simple context.
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 the input schema has 100% description coverage (though empty). The description doesn't need to explain parameters, so it appropriately avoids redundant information. Since there are no parameters, the baseline is 4, as the description correctly focuses on the tool's purpose without unnecessary parameter details.
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's purpose with specific verbs ('check', 'provide diagnostics') and identifies the target resource ('Ollama server health'). It distinguishes this from siblings like 'system_resource_check' (general system) and 'test_model_responsiveness' (model-specific), but doesn't explicitly contrast them. The purpose is unambiguous but could be more precise about what 'health' entails.
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 when health diagnostics are needed, such as before operations or troubleshooting. However, it provides no explicit guidance on when to use this tool versus alternatives like 'system_resource_check' (which might overlap) or 'start_ollama_server' (which might be a prerequisite). Usage is contextually implied but lacks clear when/when-not directives or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states it's a listing operation but doesn't describe what 'details' include, whether it requires Ollama server to be running, or how it handles errors. Significant behavioral context is missing for a tool with zero annotation coverage.
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?
Single sentence with zero waste - every word contributes essential information. Front-loaded with the core purpose, appropriately sized for a simple listing tool with no parameters.
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?
For a zero-parameter listing tool with no output schema, the description provides the core purpose but lacks important context about what 'details' includes and behavioral aspects. Without annotations or output schema, more completeness about return format and operational requirements would be helpful.
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 parameters and 100% schema description coverage, the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema already fully documents this. No additional parameter information is needed or provided.
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 specific action ('List') and resource ('locally installed Ollama models'), with additional scope ('with details') that distinguishes it from simpler listing tools. It precisely communicates what the tool does without ambiguity.
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 context (when you need to see installed models), but doesn't explicitly state when to use this tool versus alternatives like 'suggest_models' or 'select_chat_model'. No guidance on prerequisites or exclusions is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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