Ollama_MCP_Guidance

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have clearly distinct purposes: listing models, version, running status, guide, embeddings, model info, chat, and generation. The `get_api_doc_section` tool lacks a description, creating slight ambiguity, and `simple_chat` vs `simple_generate` could be confused but descriptions mitigate this.
Naming Consistency3/5
Naming mixes prefixes: `get_*` for queries, `post_*` for actions, and `simple_*` for simplified implementations. While all use underscore_case, the inconsistent verb prefix and the `simple_` prefix deviate from a strict verb_noun pattern.
Tool Count5/5
9 tools is well-scoped for an Ollama MCP server focused on guidance and basic interactions. Each tool covers a distinct aspect without unnecessary overlap or excessive quantity.
Completeness4/5
The tool set covers core operations: listing, version, running status, embeddings, model info, chat, and generation. It lacks model management (pull, delete, create) but that aligns with a guidance focus. The `get_api_doc_section` tool hints at documentation access, enhancing completeness.
Average 3.8/5 across 9 of 9 tools scored. Lowest: 1/5.
See the Tool Scores section below for per-tool breakdowns.
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- No stable releases found
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- 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?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
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 for behavioral disclosure. It describes the tool as returning a JSON string with documentation but lacks details on side effects, rate limits, or auth requirements. The tool is clearly read-only, but this is assumed rather than stated.
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 overly verbose with emojis, bullet points, and repetitive phrasing. It front-loads the purpose but then extends with excessive detail in a pushy tone. Every sentence does not earn its place; the guide's content could be summarized more efficiently.
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 tool's simplicity (no parameters, static documentation), the description covers the purpose, usage, and return format. The output schema exists, so return values are explained. It is complete enough for a guide tool with no complex behavior.
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?
There are no parameters, and schema coverage is 100%. The description explicitly states 'None' and repeats this. The baseline for 0 parameters is 4, and the description adds value by explaining the return format, so a 4 is appropriate.
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 it is a 'Getting Started Guide' for the ask-ollama project, using specific verbs like 'provides', 'contains', and 'lists'. It distinguishes itself from sibling tools like get_api_doc_section by offering a comprehensive overview rather than specific sections.
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 explicitly recommends running this guide before other operations, stating 'Before proceeding with any operations, it's highly recommended to...' and lists steps for user interaction. It does not explicitly mention when not to use it or alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the API endpoint, return format (JSON with specific fields), and error behavior ('fails, returns error information'). It does not cover authentication or rate limits but is adequate for a read-only 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 well-structured with Args and Returns sections. It is front-loaded with the purpose. However, the first sentence slightly restates the title ('Show detailed information about a model'). It is concise but not overly terse.
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 one parameter, no annotations, and the presence of an output schema (implied by return description), the description covers the return format well. It lacks prerequisites (e.g., Ollama must be running) but is largely complete for a simple retrieval tool.
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 schema has 0% description coverage, so the description must add value. It explains that the 'model' parameter is a model name with examples ('llama2', 'mistral'), but lacks deeper semantics like format constraints, validation, or list of valid values.
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 verb ('retrieves detailed information') and the resource ('model'), and specifically mentions the API endpoint '/api/show'. It distinguishes from sibling tools like get_ollama_list (which lists models) and simple_chat (which generates responses).
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 needing detailed model info but provides no explicit guidance on when to use vs. alternatives like get_ollama_list or when not to use. Sibling tool names suggest alternatives, but no comparisons or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses limitations (no streaming, no raw mode, etc.), features (basic generation, error handling, performance metrics), and return format. This provides sufficient behavioral context for an agent.
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 well-structured with clear sections (limitations, features, args, returns) and front-loaded with the core purpose. It is somewhat verbose but each section adds value.
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 simple tool (2 parameters, output schema exists), the description covers all necessary aspects: purpose, limitations, features, parameter explanations, and return format. It is fully sufficient for an agent to use correctly.
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 schema has 0% description coverage, but the description includes an 'Args' section that explains each parameter with examples ('llama2', 'mistral'), adding meaning beyond the schema's property names.
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 it is for 'basic text generation with Ollama models' and explicitly lists limitations and features, differentiating it from more advanced endpoints. However, it does not directly contrast with sibling tool 'simple_chat', which may cause 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 for simple generation without advanced features like streaming or parameter tuning, but it does not explicitly state when to use this tool versus alternatives like 'simple_chat' or 'post_generate_embeddings'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 that it queries an API endpoint, validates the response, and returns a JSON-formatted string or error details. It could be more explicit about being read-only and safe, but overall it is transparent enough.
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 fairly concise with three short paragraphs. It front-loads the purpose and then explains internals and return format. However, some details like 'similar to the ollama ps command' could be seen as slightly redundant, but overall it is well-structured.
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 no annotations, the description provides complete context: input (none), behavior (queries API, validates), and output (JSON string with list or error). With an output schema present, it does not need to detail return fields, but it still provides a useful summary. For a simple read tool, it is fully sufficient.
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 explicitly states 'Args: None: This function does not require any parameters.' Since schema coverage is 100% and the description adds clarity about no inputs, a baseline of 4 is appropriate.
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 it queries the Ollama API's /api/ps endpoint to get the status of currently running models, similar to 'ollama ps'. It distinguishes itself from sibling tools like get_ollama_list (which likely lists all models) by focusing on running models.
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 is for checking running models, but does not explicitly state when to use this tool versus alternatives like get_ollama_list or other sibling tools. No exclusions or prerequisites are mentioned, leaving some ambiguity for the agent.
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?
No annotations are provided, so the description carries full burden. It mentions that it uses the Ollama API, takes a model and text, and returns JSON with success or error. However, it does not disclose whether the operation is read-only or has side effects, nor does it discuss rate limits or authentication. This is adequate but not comprehensive.
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 well-structured with Args and Returns sections, and examples. It is fairly concise, though the return format description is slightly verbose. Every sentence serves a purpose.
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 2 required parameters, no annotations, and an output schema (though not shown), the description covers the essential inputs, expected output format, and error handling. It is complete enough for an AI agent to use the tool 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 description coverage is 0%, so the description must compensate. It does so excellently: for 'model', it gives a type, example, and format; for 'text', it specifies it is a list of strings with examples. This adds significant meaning beyond the bare schema.
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 embeddings for the given text using Ollama API.' This is a specific verb-resource pair, and with sibling tools like simple_chat and get_ollama_list, it is clearly distinguishable as the embeddings tool.
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 a clear context for using the tool (to generate embeddings) but does not explicitly state when not to use it or mention alternatives. However, the sibling tool names imply distinct purposes, so it's reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden and reveals that it queries /api/tags and returns JSON with model details or error, sufficient for a simple read 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 concise, well-structured with summary, details, and Args/Returns sections, with no unnecessary words.
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 no annotations and presence of output schema, the description provides complete information about behavior, parameters, and return format for this simple tool.
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?
There are no parameters, and the description explicitly confirms no parameters are needed, adding clear value beyond the empty schema.
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 the specific verb 'list' and identifies the resource as 'all available models in the Ollama server', clearly distinguishing it from siblings like get_ollama_version or get_running_models.
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 states that it lists all available models, implying a general use case, but does not explicitly provide guidance on when to use this tool versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes endpoint (/api/version), that it returns version info or error details. No annotations provided, so description carries burden. Could mention read-only nature, but implied.
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: three short paragraphs with front-loaded summary. No unnecessary information.
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?
Has output schema, description explains return format (JSON string with version or error). Given simplicity, it's complete. Minor oversight: no mention of potential latency or network errors, but acceptable.
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?
No parameters, schema coverage 100%, description explicitly states no args. Perfectly clear.
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 verb (get) and resource (version information of the Ollama server). Distinguishes from sibling tools which are about listing, generating, or chatting, not version retrieval.
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?
Provides context: 'useful for checking if the Ollama service is running and verifying its version.' No explicit when-not or alternatives, but siblings are clearly different, so sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: no conversation history, no streaming, no system prompts, etc. Also details error handling and performance metrics. The return format is described as JSON with response or error details.
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 well-structured with sections and bullet points, but slightly verbose (repeats 'Basic chat' in two places). Still, every sentence adds information.
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 simplicity (2 params, no nested objects) and the presence of output schema (described in return), the description covers all necessary aspects: purpose, usage, limitations, features, parameters, and return format. No gaps.
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 0%, but description adds meaning: for 'model', it gives examples like 'llama2', 'mistral'; for 'prompt', it says 'User input text'. This adds value beyond the schema's purely structural definition.
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 'Basic chat interaction with Ollama models' and distinguishes from 'complex_chat' (though not implemented). The verb 'chat' and resource 'Ollama models' are specific.
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 says when to use (basic chat) and when not (advanced features require complex_chat). Lists limitations. However, does not compare to sibling 'simple_generate', which might be another text generation tool.
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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