Ollama MCP Server

by m-mehdi14

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Latest release: v1.0.4
Disambiguation4/5
Tools are mostly distinct: model management (list, info, pull, delete) is clearly separated from inference (chat, generate). The only potential overlap is chat vs. generate, but chat is for multi-turn conversations while generate is for single completions, which is a standard distinction.
Naming Consistency3/5
Most tools follow a verb_noun pattern (list_models, get_model_info, pull_model, delete_model), but chat and generate are single verbs without a noun object. This is a noticeable deviation, though the naming remains readable and understandable.
Tool Count5/5
Six tools is well-scoped for an Ollama server, covering both model management and inference without unnecessary overlap or bloat. Each tool serves a clear purpose.
Completeness4/5
The toolset covers core lifecycle (list, info, pull, delete) and both chat and text generation. It lacks advanced features like model creation, copying, or embeddings, but these are not essential for basic Ollama interactions and can be added later.
Average 3.2/5 across 6 of 6 tools scored. Lowest: 2.4/5.
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
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This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it provides none. It does not indicate whether responses can be streamed, whether conversation context is maintained, or any side effects. The single sentence gives no insight into the tool's runtime behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, which is concise, but it is under-specification rather than effective brevity. The single sentence does not provide enough value to justify its place, as it simply restates the tool's name without elaboration.
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 has five parameters, no output schema, and no annotations, the description is far too minimal. It fails to explain return values, streaming behavior, or how the context parameter works, leaving major gaps for the 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 provides full descriptions for all five parameters (100% coverage), so the baseline of 3 applies. The description itself adds no additional semantic context, but the schema already carries the load 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 ('Chat') and resource ('an Ollama model'), making the tool's primary purpose understandable. However, it does not differentiate this from the sibling 'generate' tool, which could also be used to communicate with a model, so it lacks a distinct scope.
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 given on when to use this tool versus alternatives like 'generate'. The description does not mention conversation context, multi-turn use, or exclusions, leaving the agent without clear direction for tool selection.
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 only says 'Generate text using an Ollama model,' which reveals no behavioral traits such as streaming support, whether output is deterministic, or whether it requires model availability. The schema hints at stream and system parameters, but the description does not explain their effect or any side effects.
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, front-loaded with the action and resource. It wastes no words and is easy to scan. While it may be too brief in other dimensions, as a concise statement it is exemplary.
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 4 parameters and no output schema, the description lacks important context: no mention of what the response looks like, no usage guidance relative to siblings, and no note on prerequisites. The schema covers parameters, but overall operational context is missing.
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% (all four parameters have descriptions), so the schema already documents parameters thoroughly. The description adds no additional parameter-level meaning beyond the schema, aligning with the baseline score of 3 for full 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 states a clear verb ('Generate') and resource ('text using an Ollama model'), but it does not distinguish itself from the sibling 'chat' tool, which also generates text. It is specific enough for basic understanding but lacks differentiation.
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 like 'chat'. There is no mention of prerequisites, such as whether the model must already be pulled, or contextual use cases. The description gives no indication of when one option would be preferred over another.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description must disclose behavioral traits. It only restates the action without mentioning side effects (e.g., network usage, local storage, overwriting existing models) or prerequisites like an active Ollama server.
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 one short sentence with no extraneous words. It is well-structured and front-loaded with the action verb.
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 that downloads external resources, the description lacks essential context such as network requirements, error conditions, or what happens after download. With no annotations or output schema, the description is insufficient for an agent to fully understand implications.
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 single parameter 'model' is fully documented in the schema with a description 'The name of the model to pull'. The tool description adds no supplementary parameter semantics, so baseline 3 applies.
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 clear verb 'Pull/download' with a specific resource 'a model from Ollama'. This action is distinct from sibling tools like list_models or delete_model, which involve different operations.
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 usage context or alternatives are provided. The description does not explain when to use this tool versus sibling tools like list_models or chat, leaving the agent to infer.
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. The description only says 'Delete a model from Ollama,' which implies destructiveness but does not disclose that deletion is irreversible, whether it fails if the model is loaded, or any other behavioral traits. This is a significant gap for a destructive operation.
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 with no extraneous words. It is front-loaded with the key verb 'Delete' and clearly identifies the resource. This is appropriately concise and well-structured.
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 tool is simple with one well-documented parameter and no output schema. The description is minimal but adequate for a basic delete operation. However, the lack of behavioral warnings (e.g., permanence, side effects) makes it slightly incomplete for an agent that should anticipate consequences, hence a score of 3.
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 fully describes the 'model' parameter with 100% coverage, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides, as it simply repeats the name of the parameter without further elaboration.
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 'Delete a model from Ollama' clearly states the specific action (delete) and resource (model), making it distinct from sibling tools like list_models, get_model_info, or pull_model. This satisfies the requirement of a specific verb+resource with clear differentiation.
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, nor any prerequisites or conditions (e.g., model must not be in use, operation is permanent). It simply states what the tool does, leaving the agent without context on appropriate usage.
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 all behavioral disclosure. It only states the basic action without revealing the returned information, whether the call is read-only, or error behavior. This minimal context adds little beyond the tool's name.
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, front-loaded sentence with no redundant wording, making it appropriately concise for the tool's simplicity.
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?
With no output schema and a very sparse description, the agent lacks information about the response format or any constraints on the model name. The description is minimally viable but leaves important questions unanswered.
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 sole parameter 'model' is fully documented in the schema. The description adds no extra semantics beyond what the schema already provides, so it earns the baseline score for high schema coverage.
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 fetches information for a specific model. The term 'specific' differentiates it from the sibling 'list_models' and other action-oriented tools.
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 phrase 'specific model' implies this tool is for retrieving details about a single named model, contrasting with listing all models. However, it doesn't explicitly name alternatives or provide exclusions, so it falls short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. The verb 'List' implies a read-only operation, but the description does not explicitly state that it avoids side effects or what happens if no models are available.
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, front-loaded sentence with zero filler. It immediately identifies the action and target resource.
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 list tool, the description is largely complete. It does not specify the return format, but the absence of an output schema and the tool's simplicity keep this from being a major gap.
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 zero parameters, and the description fully captures the scope. No parameter explanation is needed, and the schema is empty, so the description is sufficient.
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 ('List') with a clear resource ('all available Ollama models'), and the scope ('all') distinguishes it from sibling tools like get_model_info, which targets a single model.
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?
There is no guidance on when to use this tool versus alternatives such as get_model_info. The description only states what the tool does but does not mention when to choose it over a sibling or any exclusions.
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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