LM Studio MCP Bridge
Server Quality Checklist
Latest release: v1.9.0
- Disambiguation2/5
Multiple tools have overlapping purposes: get_system_health, check_server_status, and lms_status all report health status; list_local_models, lms_ls, and lms_ps all list models in different forms. The distinction between query_local_llm and query_local_llm_stateful is clear but other boundaries (e.g., lms_server_control vs lms_runtime_control) are fuzzy.
Naming Consistency2/5Naming conventions are mixed: most tools use snake_case (get_system_health, query_local_llm), but a subset uses an lms_ prefix (lms_status, lms_ps, lms_get). Verb choices vary inconsistently across similar actions (list vs get vs check vs read), and the async variant `analyze_local_image_async` does not follow a systematic pattern.
Tool Count2/5With 28 tools, the set is bloated. Many tools duplicate functionality or could be consolidated (e.g., multiple health check tools, multiple model listing tools). A more streamlined set of around 15-18 core tools would better align with the bridge's purpose.
Completeness3/5The tool surface covers core workflows: model lifecycle, inference, vision, RAG, embeddings, file access, and network mesh. However, there are notable gaps such as no explicit tool to delete/unload models (only load/unload by model instance) and no way to cancel or manage background tasks beyond checking status. The redundancy in health/status commands also creates confusion but not missing functionality.
Average 3.1/5 across 28 of 28 tools scored. Lowest: 1.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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 carries the full burden of behavioral disclosure, but it only says 'manage', revealing nothing about side effects, return values, whether start/stop blocks, or what status reports. This is entirely opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, but this is under-specification, not effective conciseness. It withholds essential operational detail while offering no meaningful content in its single sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, no annotations, and a single parameter, yet the description fails to mention the available actions, return behavior, or how it differs from several similar sibling tools. The agent cannot reliably select or invoke this tool based solely on the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for explaining the 'action' parameter, but it doesn't mention it at all. The enum values are in the schema, but the description itself adds no semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the generic verb 'manage' and basically restates the tool name ('lms_server_control' -> 'Manage the local inference server'). It doesn't specify the actual operations (start/stop/status) and doesn't distinguish from sibling tools like lms_runtime_control or check_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 Guidelines2/5Does 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 such as lms_runtime_control, lms_status, or load_local_model. The description provides no context, prerequisites, or exclusions.
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?
With no annotations provided, the description must disclose behavioral traits, but it gives none. It does not mention side effects, permission requirements, whether it starts a server, or how it interacts with the system. This is a significant omission for a loading tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief but not effectively concise; it is under-specified. The single sentence 'CLI: Advanced model loading with granular control' conveys little concrete information and could apply to almost any model-loading tool, so it does not earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no annotations and no output schema, the description needs to provide context about what the tool does, its parameters, and its effects. It fails to do so. The tool has 4 parameters and potentially complex behavior (GPU offload, context length), but the description offers no context, making it inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema describes 3 of 4 parameters (gpu, identifier, context_length), giving 75% coverage. The description adds no extra parameter semantics beyond the schema, so with high schema coverage the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'loading' and the resource 'model', so it communicates the basic action of loading a model. However, terms like 'advanced' and 'granular control' are vague and do not specify what advanced means, nor does it differentiate this tool from sibling tools like 'load_local_model' or 'lms_get'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. The description does not mention exclusions, prerequisites, or scenarios where another tool would be more appropriate. Sibling tools include 'load_local_model' and 'lms_get', but the description provides no comparison.
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 full burden. It only says 'Manage and update' which hints at mutation but does not disclose side effects, permissions, reversibility, or output behavior. The agent cannot anticipate what happens with actions like 'update' or 'survey'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence but is under-specified. The 'CLI:' prefix adds no value, and the sentence is too terse to convey the tool's functionality. It is not effective conciseness; it is minimalistic to the point of being unhelpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has five distinct actions, no output schema, and no annotations, yet the description provides almost no context. The agent is left without information about what each action does, when to use them, what the target should be, or what results to expect. This is critically incomplete, especially given many similar sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no meaning to the parameters. The schema describes only the 'target' field, leaving 'action' with an enum of five commands but no explanation. With 50% schema coverage, the description should clarify the actions and target format, but it remains silent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a resource ('inference runtime engines') and implies control actions with 'Manage and update', but it is vague and does not distinguish this tool from sibling tools like lms_server_control or lms_ls. It is not a tautology, but it lacks specificity about the exact scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. Sibling tools such as lms_server_control, lms_status, lms_ls, and lms_get likely overlap, but the description does not mention any exclusions or preferred contexts.
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 states the action (list) but does not disclose the return format, whether it is a read-only operation, or how the 'detailed' parameter affects behavior. This is minimal disclosure at best.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short sentence with no redundancy, making it very concise. However, it is so brief that it misses important information, but conciseness itself is good.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional param, no output schema), a fuller description could still clarify the meaning of 'detailed' and what 'available' vs 'loaded' means. The current description leaves significant gaps about return values and parameter effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a single 'detailed' boolean with no schema-level description (0% coverage), and the tool description does not mention this parameter at all. The agent has no way to know what 'detailed' does, making parameter semantics completely unhelpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does 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 'loaded and available models,' which clearly states the tool's function. However, it does not distinguish this from sibling tools like lms_ls or query_local_llm, which may also list models, so it lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 such as lms_ls or query_local_llm. There is no mention of use cases, prerequisites, or exclusions.
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 does state that analysis runs locally and is privacy-focused, which is a key trait. However, it omits other critical behaviors such as requiring a loaded model, the nature of the response, or any side effects, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the tool's purpose and primary differentiator (privacy). It is front-loaded and free of fluff, though it could include a bit more operational detail without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, the description is insufficiently complete. It does not explain what the tool returns, how the prompt interacts with the image, or what prerequisites exist (e.g., models loaded). Given the rich sibling context, more detail is necessary for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67%, with prompt and image_path described. The description adds value by naming example models (Llava, Moondream) that give meaning to the undocumented 'model' parameter, but it does not elaborate further on parameter semantics. This partial compensation results in a mid-range score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as privacy-focused image analysis using local vision models, naming specific models like Llava and Moondream. It distinguishes it from sibling tools by emphasizing local execution and privacy, though it does not explicitly mention the image_path and prompt inputs or the textual output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a use case for privacy-sensitive image analysis but provides no explicit when-to-use guidance or alternatives. It does not mention the async sibling tool analyze_local_image_async or other local LLM query tools, leaving the agent without direction on choosing this over 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the core function and omits important details such as output format, model selection behavior, blocking behavior, or requirements like having a model loaded.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, grammatically complete sentence that expresses the core purpose without redundancy. It is well-structured and front-loaded, earning a high conciseness score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, and the description does not specify what the returned vector representation looks like (e.g., array of floats, dimensionality, model used). It also omits operational context like model loading, making the tool usage ambiguous for agents.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already describes the input and model parameters. The description adds no additional parameter context, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('generate') and resource ('vector representations of text'), clearly indicating embedding generation. It distinguishes from sibling tools such as query_local_llm or analyze_local_image, though it does not explicitly name alternatives or scope constraints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 query_local_llm or search_local_docs. The description does not mention prerequisites, use cases, or exclusions, leaving the agent to infer from the tool name.
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 fails to mention that downloading writes files to disk, requires network access, or may modify the local model registry. This lack of side-effect information is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with no wasted words, making it concise and easy to parse. It lacks depth but is appropriately sized for the limited scope described.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool can perform two distinct actions (search vs. download) and has no output schema, the description fails to explain return values, error conditions, or behavior when no model is found. It also does not account for individual parameter combinations, leaving the agent with incomplete expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (2 of 2 parameters described), so the schema carries the parameter semantics. The description adds minimal meaning beyond the schema, only implying that 'query' is used for searching and 'download' triggers a download.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for or downloads models from LM Studio Hub, using specific verbs and a resource. It distinguishes from sibling tools that operate on local models (e.g., lms_ls, list_local_models), though it could be more precise about the search result format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 like lms_import or lms_ls. It does not mention any prerequisites, use cases, or exclusions, leaving the agent without context 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, the description carries the full burden of disclosing behavioral traits. It only states 'Import a model file' without explaining side effects, permissions, the fate of the original file, or what 'import' entails within LM Studio, leaving significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no redundant words. It is front-loaded and appropriately sized for a tool with one parameter, earning maximum points for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with no output schema and no annotations, but the description is insufficiently complete. It does not mention prerequisites, expected outcomes, or clarify the distinction from sibling tools like lms_load_cli, leaving the agent with incomplete context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage for the only parameter 'path', describing it as 'Path to the model file (.gguf).' The description adds no additional parameter semantics beyond what the schema already states, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool imports a model file into LM Studio, using a specific verb and resource. However, it does not differentiate from sibling tools like lms_load_cli, which also handles models, so it lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 does not mention any exclusions, prerequisites, or compare with sibling tools such as lms_load_cli or lms_ls.
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 must carry the burden. It mentions 'Privacy-First' implying local-only processing, but doesn't disclose behavioral details such as file access requirements, return format, or potential 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise and front-loaded with the key purpose. No wasted words, though it's minimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and minimal annotations, the description is insufficiently complete. It doesn't explain what the response looks like, how the file is processed, or any caveats about local LLM querying.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and both parameters have clear descriptions in the schema. The tool description adds no additional parameter semantics beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: reads a local file and queries the local LLM about its contents. It differentiates from siblings like read_file_content and query_local_llm by combining both actions, but doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like read_file_content or query_local_llm. The 'Privacy-First' tag hints at local processing but doesn't provide clear usage context or exclusions.
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 present, so the description carries full behavioral burden. It states the action (setting a preferred device) but fails to disclose side effects, persistence, error conditions, authorization needs, or impact on remote model resolution beyond the basic phrase. This is insufficient for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the domain category 'Mesh & Network'. It is efficient and easy to parse, though it is too brief to include deeper context that would make it more useful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple setter with one parameter and no output schema, the description is minimally viable but lacks important context about when to use it, implications for the mesh/network, and relationship to sibling tools like manage_lm_link. It answers 'what' but not 'why' or 'when'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage for device_name with a clear description. The tool description does not add any additional parameter semantics beyond restating the preference concept; baseline for high schema coverage is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Set' and a clear resource 'preferred LM Link device', with a qualifying phrase 'for remote model resolution'. This clearly communicates the action and scope, and it subtly differentiates from broader sibling tools like manage_lm_link, though it does not explicitly contrast alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. There is no mention of prerequisites, use cases, or exclusions, leaving the agent without insight into how this tool fits into the broader workflow.
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 must carry the full burden. It discloses that the operation is asynchronous and returns a Task ID, but it lacks details about task progression, result retrieval, or prerequisites. This is insufficient for an async tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, conveying the essential purpose and return value in a single sentence. It is front-loaded and contains no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is an async operation with no output schema, and the description only mentions the Task ID. It fails to explain how to retrieve the result or tie into the available `get_bridge_task_status` tool, leaving a significant contextual gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. However, it does not explain any of the parameters (`image_path`, `prompt`, `model`). The property names are self-explanatory, but the description adds no additional meaning or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool starts a background image analysis task and returns a Task ID. The 'Async' in the name and 'background' in the description distinguish it from the synchronous sibling `analyze_local_image`. It uses a specific verb ('start') and resource ('image analysis').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for non-blocking image analysis, but it does not explicitly state when to use this over the synchronous `analyze_local_image` or how to check task status. No alternatives 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.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. 'Verify' implies a read-only operation, but it does not disclose whether a network request is made, what the return format is, or if any side effects occur. The absence of an output schema makes this gap more significant.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler. It is front-loaded with the verb and resource, making it extremely concise and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description should explain what the tool returns (e.g., a status object vs. a boolean) and any failure modes. It does not, leaving a notable gap for a zero-configuration tool whose behavior might otherwise be assumed to be trivial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the rubric baseline is 4. The description adds no parameter-specific semantics because there are none to explain.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('verify') and resource ('LM Studio API connection and health'), clearly indicating the tool's function. It does not explicitly distinguish from sibling tools like get_system_health or lms_status, but the phrase 'LM Studio API' narrows the scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 such as lms_status or get_system_health. There are no exclusions, prerequisites, or contextual hints about when this check is appropriate.
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, the description carries the full burden of behavioral disclosure. It only states the action without mentioning whether it is read-only, what happens if the task ID is invalid, or what the response contains. No side effects or error behaviors are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no redundant information. It front-loads the core purpose and is appropriately sized for a simple tool, earning a high score for conciseness despite its lack of depth.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description should explain what 'status and result' means and how to interpret the response. It also fails to mention the tool's relationship to async task creation, making the description incomplete for practical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, and the description does not explain the 'task_id' parameter at all. It does not indicate where the task ID comes from or any format expectations. Since the description must compensate for the schema's lack of information and fails to do so, the score is low.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: checking the status and result of a background vision task. It uses a specific verb ('Check') and resource, which distinguishes it from sibling tools like get_system_health or check_server_status by explicitly scoping to vision tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. It does not mention that it should be used after starting an async task via analyze_local_image_async, nor does it provide any context on prerequisites or exclusions.
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 'CLI:' but doesn't explain what that implies (e.g., external command execution, side effects, or output format). The read-only nature is implied by 'List' but not explicitly stated, and there is no detail on error behavior or interaction with the local server.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that is front-loaded with the verb and resource. Every word serves a purpose and there is no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (no parameters, no output schema), the description could still be more complete by explaining what 'loaded in memory' means, what the return value looks like, and how it differs from similar tools like 'list_local_models' or 'lms_ls'. As written, it leaves ambiguity in a toolset with many closely related options.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is fully covered. The description doesn't need to elaborate on parameter semantics, and the baseline of 4 applies as there are no parameters to clarify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and a specific resource ('models currently loaded in memory'), making the primary purpose unambiguous. However, it does not explicitly distinguish this from sibling tools like 'list_local_models' or 'lms_ls', which could overlap in scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. The description merely states what it does without any context on prerequisites, typical scenarios, or exclusions.
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, the description carries the full burden of behavioral disclosure. It mentions loading into LM Studio memory, implying state change and resource usage, but does not disclose side effects such as whether an already-loaded model is replaced, whether server must be running, or whether the operation is asynchronous or expensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the core action and target. There is no fluff, and every word contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no annotations and no output schema, the description provides the essential purpose but lacks operational context such as expected outcomes, error scenarios, or follow-up actions. It is minimally viable but not rich enough for an agent to fully anticipate behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only one parameter, model_id, and the description adds minimal meaning by calling it a 'specific model'. However, schema_description_coverage is 0%, and the description does not explain the expected format, source, or how to obtain valid model IDs, though the parameter is fairly self-evident from the tool name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Load') and resource ('specific model into LM Studio memory'), making the primary purpose obvious. It is distinct from unload_local_model but does not explicitly differentiate itself from lms_load_cli or other similar loading tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or comparison with sibling tools like lms_load_cli or unload_local_model, leaving the agent to infer usage context.
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 'Retrieve', implying read-only, but does not state return format, side effects, or access requirements. For a tool with no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence without unnecessary words. It is appropriately sized for a simple, no-parameter retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter read tool, the description is minimally adequate. However, there is no output schema, so the description should ideally mention what the response contains or clarify what 'bridge' refers to. It leaves room for interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the baseline score is 4. The description adds no parameter information, but there is nothing to document.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the current bridge environment and network configuration. The verb 'Retrieve' and specific resource differentiate it from siblings like get_system_health and check_server_status, though 'bridge' is not explicitly defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not indicate when to use this tool versus alternatives such as get_lm_link_status or get_bridge_task_status, nor does it mention any exclusions.
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 available, the description carries full responsibility for behavioral disclosure. However, it omits details such as whether the listing includes subdirectories, hidden files, or ordering, and does not describe the output format. This is a lean description for a potentially simple tool, but more transparency would be expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that directly conveys the tool's purpose without extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description gives the basic function but omits usage context, return values, and edge-case behavior. The siblings list suggests related tools, but the description does not situate itself among them. This is minimally viable but leaves the agent without enough information for confident invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no descriptions for directory_path (0% coverage). The description's phrase 'a specific local directory' gives some context that the parameter should be a local path, but lacks specifics like absolute/relative paths or validation behavior. The parameter name is self-explanatory, so this is adequate but not enriching.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (List) and the resource (files in a specific local directory). It distinguishes from siblings like read_file_content by focusing on directory listing rather than file content reading.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 over alternatives. It lacks any mention of suitable scenarios, prerequisites, or alternative tools for different needs (e.g., recursive listing vs simple listing).
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 the 'CLI' nature but does not state whether this is a read-only operation, what the output format is, or any potential side effects. The description adds little beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no wasted words. It front-loads the verb and resource effectively.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (no params, no output schema), the description is minimally viable. However, it lacks context about how it differs from similar health/status tools, which could lead to ambiguity in tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema is complete (100% coverage with no params). The description does not need to explain parameters, and the baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Show') and resource ('overall health of the LM Studio daemon and server'). It is specific enough to understand the tool's purpose, but it does not differentiate from similar sibling tools such as check_server_status or get_system_health.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. It does not mention any context, prerequisites, or exclusions, leaving the agent without decision support 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, the description carries the full burden of behavioral disclosure. It only states the basic action without explaining side effects, output format, blocking behavior, or whether this is a read-only operation. For a CLI tool, important traits like whether logs are printed to stdout, saved to a file, or require specific permissions are undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, information-dense sentence that gets straight to the point. It front-loads the action and resource, with no redundant words. Every word contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter, no output schema, and no annotations, the description is adequate but leaves gaps. It doesn't explain what happens with the captured logs (e.g., displayed, saved) or how the output is delivered. Given the simplicity, the missing context on return behavior and potential side effects makes it only minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents the sole parameter 'duration_ms' with a clear description ('How long to capture logs in milliseconds'). The tool description adds only a vague qualifier 'brief duration,' which does not meaningfully expand on the schema. Since schema coverage is 100%, baseline of 3 is appropriate; no additional param semantics are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Capture a snapshot of current logs for a brief duration.' It specifies the resource (logs) and the action (capture snapshot), which distinguishes it from sibling tools like lms_status or lms_ps that handle other aspects of system state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided regarding when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or scenarios where another tool would be preferred. The description is purely definitional, offering no context on how to choose between this and sibling log-related commands.
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, the description carries the full burden of behavioral disclosure. It states 'directly read' which hints at no transformation, but it does not disclose error handling (e.g., file not found), permissions, encoding, or return format. This is minimal behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently states the tool's purpose. Every word earns its place, with no wasted tokens.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations and output schema, the description is too sparse. It does not cover when to use it versus siblings, error scenarios, or what the return value looks like. While the tool is simple, this is not enough for an agent to use it with full confidence.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter, file_path, covered at 0%. The description adds only 'local file,' providing some context that file_path is a local filesystem path, but it does not explain formats, absolute vs. relative paths, or other semantics. It does not compensate for the lack of schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'read' and resource 'local file,' clearly stating that it returns content. It distinguishes itself from siblings like list_files_in_directory (listing) and query_local_file (querying) by emphasizing 'directly read the content.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: use this when you want to read raw local file content. However, it does not explicitly mention when not to use it or provide alternatives, such as query_local_file or search_local_docs, so it only provides implied guidance.
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. It only says 'check' resource usage, which implies a read-only operation, but it does not disclose what specific metrics are observed, whether the check is instantaneous, or any side effects. The behavior is under-specified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short, direct sentence that immediately conveys the purpose. Every word is useful and there is no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description gives a basic understanding of its purpose. However, it does not mention what the returned resource-usage information looks like or whether it returns structured data, which leaves the tool somewhat incomplete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema fully covers parameter semantics. The description appropriately adds no parameter details because none are needed, meeting the baseline for no-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Check') and a specific resource ('bridge machine and LM Studio host resource usage'). It is more specific than generic sibling tools like check_server_status, though it doesn't explicitly distinguish itself from all possible overlaps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool—when checking resource usage—but does not explicitly state when to prefer it over sibling tools like check_server_status or get_bridge_task_status. There are no exclusions or alternative guidance.
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, the description carries the full burden of behavioral disclosure. It only states the action without explicitly noting that the operation is read-only, non-destructive, or what side effects (if any) might occur. The 'on disk' qualifier adds minor context but lacks details about return format or system state changes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no superfluous words. It gets directly to the point and is easy to parse, making efficient use of the available space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter, no-output-schema tool, the description is adequately complete to convey the core function. However, it omits any mention of the return value (e.g., a list of model names), which would be helpful for an agent to anticipate the tool's result. The lack of annotations and output schema is partially offset by the simplicity of the operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline score is 4. The description correctly avoids inventing parameters and does not need to compensate for schema gaps since there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (list), the resource (models), and a specific scope (currently available on disk). This effectively distinguishes it from siblings like lms_ps (processes) and lms_get (single model retrieval), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided regarding when to use this tool versus alternatives. There is no mention of preferred contexts, exclusions, or relationships to sibling tools such as list_local_models or lms_load_cli, leaving the agent to infer usage from the name 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It indicates a 'local' and read-only activity, but does not state whether it modifies files, whether it indexes on the fly, or what performance characteristics to expect. The description mostly restates the tool's purpose without deeper behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no redundant wording. The 'Local RAG' prefix provides immediate context, and every remaining word contributes to the core purpose. It is optimally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description is incomplete. It does not describe what the search returns (e.g., file paths, text snippets, relevance scores), nor does it clarify the role of the 'extension' filter beyond what the schema indicates. For a tool meant to search local docs, the expected output is critical and left undefined.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 no extra meaning beyond the schema: it does not explain the 'extension' default, the exact format of 'directory_path', or how 'query' interacts with the search. Since the schema already covers all 3 parameters, the description doesn't need to add more, but it also provides zero supplementary semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the exact action ('semantic search'), the target resource ('a directory'), and the method ('local embeddings'). This clearly distinguishes it from sibling tools like list_files_in_directory or query_local_llm, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Local RAG' prefix implies a use case (retrieval augmented generation) but does not explicitly state when to prefer this tool over similar alternatives like query_local_file or get_local_embeddings, nor does it provide exclusions or prerequisites.
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. It states the effect (free memory/VRAM) but does not disclose reversibility, permission requirements, or any side effects beyond memory release. This is a significant gap for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundant words. It efficiently states the action and purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers the core purpose. However, missing usage guidelines and behavioral details leave the description incomplete for an AI agent that needs to know when and how to invoke the tool safely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one required parameter (model_id) with no description, and the tool description also does not explain it. Schema description coverage is 0%, and the description does not compensate. However, the parameter is self-explanatory given the tool name, so the impact is minor.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'unload' with a clear resource 'model instance' and states the purpose 'to free up memory/VRAM'. It clearly distinguishes from sibling tools like load_local_model and list_local_models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'to free up memory/VRAM' implies when to use the tool, but there is no explicit guidance on prerequisites or alternatives. It doesn't state when not to use it or whether to check loaded models first.
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 must carry the full burden. It only lists actions without disclosing side effects, permissions, or impact (e.g., whether disabling LM Link disrupts network connections). This is insufficient for a management tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently lists the actions. It avoids unnecessary words, though it could be slightly more detailed without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given 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 should explain return values, side effects, and prerequisites. It does none of these, leaving the tool's full behavior unclear for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes the 'name' parameter as required for set-device-name, and the action enum values are self-explanatory. The description adds little beyond restating the actions, but the schema covers enough (50%) to make the parameters understandable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: enabling/disabling LM Link or setting the local device name. It uses specific verbs and distinguishes from siblings like get_lm_link_status (status checking) and set_preferred_lm_link_device (preferred device selection).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (for enabling/disabling or setting device name) and differentiates it from status and preference tools, but does not explicitly state when not to use it or mention alternatives. The context is clear enough for basic selection.
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 present, so the description carries the full burden. The word 'Display' implies a read-only operation, which is useful, but the description does not disclose whether this triggers a live network scan, requires the LM Link server to be running, or what happens when no devices are discovered. It is minimally transparent but lacks behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of under 15 words, with the category prefix 'Mesh & Network' front-loading context. Every word contributes to identifying the tool's purpose, with no filler or redundant elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple status-query tool with no parameters, no annotations, and no output schema. The description conveys the core purpose but omits details about the structure or content of the status information, whether devices are returned as a list, and any operational prerequisites. It is adequate for a trivial tool but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the input schema's properties object is empty, so schema coverage is trivially 100%. With no parameters, there is nothing for the description to add regarding parameter semantics; the baseline of 4 for 0-parameter tools applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Display') and names the exact resource ('status of LM Link and discovered devices on the mesh'). This clearly distinguishes it from sibling tools like manage_lm_link and set_preferred_lm_link_device, which imply control or modification rather than status retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 does not mention any prerequisites, exclusions, or scenarios where a sibling tool would be more appropriate, leaving the agent to infer usage purely from the tool name.
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 discloses the stateless nature (starting new conversation) and supported features. It does not mention side effects or resource implications, but for a query tool this is acceptable. It could add more, but it provides useful behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with 'Standard', every clause earns its place. It is concise without sacrificing key usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (15 parameters) and the absence of annotations/output schema, the description is too brief. It lacks parameter details, error behavior, and output format expectations, leaving significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 13%, so the description must compensate heavily. It only implies parameters for Vision and JSON Schema but offers no explanation for the other 12 parameters (temperature, top_k, penalties, etc.). This is insufficient for a 15-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool starts a new conversation or handles one-off tasks, distinguishing it from sibling query_local_llm_stateful. It also lists key capabilities (Vision, JSON Schema, Reasoning), making the purpose specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use (starting new conversations, one-off tasks) and when not to use (follow-ups with a response ID), effectively guiding the agent to the stateful sibling. This provides clear usage context.
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 discloses key behavioral traits: statefulness, speed advantage, server-side context preservation, and the prerequisite of a previous response ID. However, it does not describe return format or failure behavior, but the main behavioral context is covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the primary use case. Every sentence adds value, with no redundant or irrelevant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description defines the intended context (follow-up conversations) well and differentiates from siblings. However, it omits information about the return value or error cases, and with no output schema, the description is incomplete for fully understanding the tool's behavior beyond selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 33%, but the description crucially clarifies the role of `previous_response_id` (the key parameter) and ties it to the tool's purpose. It does not elaborate on other parameters like temperature or max_tokens, which remain unexplained, leaving some compensation gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: continuing an existing conversation using a `previous_response_id`. It uses the verb 'continuing' and implicitly contrasts with the sibling 'query_local_llm', making it specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage guidance: 'Use this ONLY for continuing an existing conversation when you have a `previous_response_id`.' It also gives reasons why (faster, preserves context server-side), effectively telling when and 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.
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