nvidia-nim-mcp

Server Quality Checklist

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Latest release: v2.1.2
Disambiguation4/5
The tools are mostly distinct, with clear purposes for embeddings, reranking, image generation, and listing models. Minor overlap exists among chat_completion, text_generation, and function_calling (all text generation) and between analyze_image and multimodal_task, but descriptions clarify their different modes.
Naming Consistency3/5
All names use snake_case, but they mix verb-first patterns (create_embeddings, list_models) with noun-first patterns (chat_completion, text_generation) and gerunds (function_calling). This is readable but not fully consistent.
Tool Count5/5
11 tools is well-scoped for the NVIDIA NIM domain, covering language, embedding, reranking, vision, and image generation. Each tool fills a specific niche without feeling excessive or thin.
Completeness4/5
The surface covers the main NIM inference workflows: chat, text generation, embeddings, reranking, function calling, vision, and image generation. Minor gaps such as streaming or fine-tuning exist, but they are not core to the server's apparent purpose.
Average 3.4/5 across 11 of 11 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 20 commits in the last 12 weeks
- Last stable release on August 17, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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

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 does reveal that the model decides which function to call and with what arguments, which is important, but it omits details about the response format, whether tools are executed, pagination, rate limits, or the tool_choice behavior. This is minimal disclosure for a tool with no annotation support.
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 only two sentences with no filler or redundancy. It is efficiently front-loaded, but it might be too sparse to be considered excellently structured, hence a 4 rather than a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, 2 required, no output schema, no annotations), the description is severely incomplete. It only mentions the high-level capability and gives no information about required inputs, return values, or edge cases. This is far below the minimum viable description for such a tool.
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?
The schema has 6 parameters with 0% description coverage, so the description must compensate. It fails to do so: it does not explain the purpose of 'model', 'tools', 'messages', 'max_tokens', 'temperature', or 'tool_choice'. The only indirect hint is 'function calling' and 'with what arguments,' which vaguely relates to 'tools' but adds no concrete parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool uses NIM models with tool/function calling capabilities, which is a specific verb+resource+capability. It clearly indicates the tool's role but does not explicitly distinguish it from sibling tools like chat_completion, though the function-calling focus provides some differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use NIM models with tool/function calling capabilities' but gives no guidance on when to choose this tool over alternatives like chat_completion or text_generation. There are no explicit exclusions, prerequisites, or recommended scenarios, leaving the decision to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden of behavioral disclosure. It only describes the action of sending a conversation to multimodal models, with no information about API key requirements, rate limits, response format, error handling, side effects, or the behavior of the model call. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise: two short sentences that immediately convey the tool's purpose without any filler. It is front-loaded with the core function, and every word earns its place, making it a model of efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no output schema, and no annotations, this two-sentence description is insufficiently complete. It fails to explain the meaning of the model, temperature, max_tokens, stream, and top_p parameters, and it provides no information about return values or potential errors. An agent would struggle to invoke this tool correctly without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33% (model and messages have descriptions; top_p, stream, max_tokens, temperature do not). The description adds no meaningful detail for the undocumented parameters—it only vaguely references the messages content. This does not compensate for the low schema coverage, leaving users without a clear understanding of the other parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: perform multimodal tasks by sending a conversation with mixed text and image content to multimodal models. It uses specific verbs ('perform'/'send') and a concrete resource, and it differentiates from text-only or image-only sibling tools by emphasizing the combination of modalities, though it 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for multimodal reasoning with both text and images, but it doesn't provide explicit guidance on when to use it versus alternatives like chat_completion or analyze_image. There is no mention of when-not-to-use or alternative tools, so the usage context is only implied, not clearly stated.
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 the full burden. It reveals that the tool depends on NVIDIA NIM models and expects a URL, but it does not mention auth requirements, network/API dependence, rate limits, public URL accessibility, or how the optional model parameter is defaulted. This is a meaningful gap for an externally backed tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the tool's purpose and key usage steps. Every word earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description gives enough core context for a simple image-analysis use case and mentions expected output types, but with no output schema and no annotations it does not cover model defaulting, call latency, or failure behavior. It is minimally viable for a schema-rich 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?
Schema description coverage is 100%, so the input schema already explains each parameter. The description adds little beyond paraphrasing image_url and prompt (URL and question), and does not clarify model selection or default behavior.
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 uses a specific verb-resource pair ('Analyze and describe images') and names the technology (NVIDIA NIM vision and multimodal models). It does not explicitly contrast with sibling tools like multimodal_task, but the image-analysis focus is distinct from generation/completion siblings.
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?
It provides direct instructions ('Provide an image URL and a prompt/question'), implying use for image analysis, captioning, or visual Q&A. However, it does not explain when to choose this tool over multimodal_task or other siblings, nor does it mention 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 provided, so the description must carry the full burden of behavioral disclosure. It discloses 'Get' as a read operation but does not specify what details are included, whether the response is paginated, or any other behavior. The value added beyond the name is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It efficiently communicates the purpose without any filler or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description is minimally adequate. However, without an output schema, it leaves the agent guessing about what 'detailed information' includes. Enumerating example fields (e.g., model ID, endpoints, parameters) would make it more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'model_id' ('The model ID'). The description does not add any additional meaning beyond the schema, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Get') and a specific resource ('detailed information about a specific NVIDIA NIM model'). The word 'specific' distinguishes it from sibling tools like list_models and compare_models, which operate on multiple models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It implies usage for a single model but does not explicitly mention when not to use it or how it relates to list_models or compare_models.
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 mentions 'List' which implies a read-only operation, but it does not disclose return format, pagination behavior, authentication requirements, rate limits, or the fact that 'detailed metadata' is only returned when include_details=true. The description adds little beyond the obvious read-only nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys purpose, filtering capability, and target use case without any fluff or repetition. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 10 optional parameters, no output schema, and no annotations, the description is very brief. It does not mention what metadata is actually returned, whether results are paginated, how to handle missing parameters, or any defaults beyond what the schema provides. The tool has moderate complexity and the description leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 10 parameters. The description adds minimal extra meaning by mentioning 'category' and listing example categories like language, embedding, reranking, etc., but this is largely redundant with the schema's enum. It also refers to 'advanced filters' without detailing them, adding little semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb-resource combination: 'List available NVIDIA NIM models'. It clearly states the scope (available models) and mentions filtering by category, distinguishing it from sibling tools like get_model_info (which presumably retrieves details for a specific model) and compare_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 phrase 'for agent selection' provides some context for when the tool might be appropriate, implying it is used during model discovery. However, it does not explicitly state when to use it instead of alternatives like get_model_info or compare_models, nor does it mention any 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?
With no annotations, the description must carry the behavioral disclosure burden. It only states the basic operation and lists supported models; it does not disclose response format, authentication requirements, token usage, or the need to include full conversation history for multi-turn context. This 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each earning its place. The first sentence encapsulates the core action; the second adds model compatibility. No redundancy, front-loaded with the primary purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 8 parameters, no output schema, and no annotations, the description leaves major gaps: no return value explanation, no guidance on message structure, and no relationship to sibling tools beyond the multi-turn hint. The description is insufficient for an agent to use the tool effectively without significant schema inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low at 25% (only model and system_prompt described). The description does not compensate for the omitted parameter meanings, failing to explain essential parameters like messages, temperature, max_tokens, top_p, seed, or stop. It adds no semantic value beyond the 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 the tool's function: 'Send a multi-turn conversation to a NVIDIA NIM language model and receive a completion.' It uses a specific verb (send), resource (conversation to NIM model), and outcome (completion). The mention of 'multi-turn' distinguishes it from sibling text_generation.
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?
While no explicit alternatives are named, the description establishes a clear use case: multi-turn conversations. This implies it should be used when conversational context is needed, as opposed to single-turn tools. However, it lacks explicit when-not-to-use guidance or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It only restates the conversion action and lists use cases; it does not mention default model behavior, truncation handling, output format specifics, rate limits, or other operational traits. This is a limited disclosure beyond the name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core action and resource before adding valuable use-case context. Every word earns its place; there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description does not explain return value shape, default model/truncation settings, or how the optional parameters affect behavior. It is adequate for a high-level understanding but insufficient for reliable invocation in complex scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes 'input' and 'model', but 'truncate' and 'encoding_format' are only enums without detailed descriptions. The tool description adds no parameter-level meaning and does not compensate for the 50% schema coverage gap. It merely restates that text is converted, which is already implied by the 'input' parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Convert') and identifies the exact resource ('text(s) into vector embeddings using NVIDIA NIM embedding models'). It clearly distinguishes from sibling tools like chat_completion and rerank_passages by stating the core function.
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 concrete use cases ('semantic search, RAG, clustering, and similarity comparisons') that signal when to use this tool. It lacks explicit exclusionary guidance or comparison to alternatives, but the context is clear enough for the agent to select it appropriately.
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 restates the basic function (generate text) and gives task examples, without disclosing any operational behaviors like response format, latency, rate limits, or authentication requirements. This 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loaded with the core action and offering a clear value proposition. No filler or redundant wording; every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters and no output schema, the description is too minimal. It omits important context about parameter usage, expected behavior, or what the response contains. The examples are helpful but do not compensate for the lack of operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 17%, covering just the prompt parameter. The description mentions 'single prompt' but adds no explanation for stop, model, max_tokens, temperature, or system_prompt. With such low schema coverage, the description needed to compensate but did not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates text from a single prompt, and explicitly positions it as a simplified interface for one-shot tasks. Examples like summarization, translation, extraction, and Q&A make the purpose concrete and help distinguish it from likely siblings such as chat_completion.
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?
It gives explicit context for when to use (one-shot tasks) but does not provide exclusions or alternatives. The phrase 'simplified interface' hints at being an alternative to more complex siblings, but no direct comparison or when-not-to-use guidance is given.
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 the burden. It discloses the ability to save PNGs to disk and notes free-tier model availability. However, it lacks information about output formats (URL vs b64_json), rate limits, or model-specific constraints beyond what the schema already states, leaving gaps in full behavioral disclosure.
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 three concise sentences, front-loaded with the primary purpose, followed by key capabilities and a practical note about free-tier models. Every sentence adds value without unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 16 parameters and no output schema, the description provides a useful overview but omits important return-value semantics (URL vs base64 JSON) and does not mention image-to-image editing via FLUX Kontext despite the schema having an image parameter. It covers core context but is not fully complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds a high-level summary (supports resolutions, samplers, schedulers) but does not provide additional meaning beyond what each parameter's schema description already offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates images from text prompts using specific NVIDIA NIM models, which distinguishes it from siblings like text_generation or analyze_image. The verb+resource is 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (image generation) and hints at model selection (free tier for FLUX models). It doesn't explicitly exclude alternatives or name them, but the sibling tools are distinctly different tasks, making the usage context apparent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It states that reranking uses NVIDIA NIM models, but it does not explicitly mention that the operation is read-only or describe any side effects, rate limits, or permission requirements. The term 'rerank' implies a pure computation, but the description could be more transparent about expected behavior such as output format or whether the original order matters.
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 two sentences, front-loaded with the primary action and resource. Every sentence is purposeful: the first states what it does, the second provides context for use. There is no redundant phrasing or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 5 parameters, no output schema, and no annotations. The description gives the core purpose and a use-case hint (RAG pipelines), but it does not explain what the tool returns, how truncate or top_k affect results, or any constraints. While the schema covers parameter details, the lack of output information leaves a gap in completeness.
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 already describes most parameters (model, query, top_k, truncate), leaving only 'passages' with no description. The tool description mentions 'query' and 'passages' but does not add meaningful semantics beyond naming them. It does not explain the oneOf structure for passages or clarify top_k behavior. With 60% schema coverage, the description provides minimal additional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reranks passages by relevance to a query, specifying the action (rerank), the resource (list of passages), and the technology (NVIDIA NIM models). It distinguishes itself from sibling tools like text_generation and create_embeddings by focusing on reranking for retrieval quality. The mention of RAG pipelines further clarifies the intended use.
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 identifies a clear use case: 'Essential for RAG pipelines to improve retrieval quality.' This provides context for when to use the tool, but it does not explicitly list alternatives or exclude other scenarios. The context is strong enough to guide an agent toward this tool for reranking tasks, though it lacks explicit alternative comparisons.
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 the full burden. It discloses the output type ('structured comparison table') and decision factors, but does not mention potential behavior around invalid model IDs, performance, or error handling. This is acceptable for a read-only comparison tool but leaves some uncertainty.
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 two concise sentences, front-loaded with the core action and key factors. Every word earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a single parameter and no output schema, the description covers the purpose, input range, and output structure. It could be slightly more explicit about what 'structured comparison table' entails, but the listed factors give a clear picture.
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 description covers 100% of the parameter with a clear explanation and example. The description adds no new semantic information beyond the schema, only reinforcing the 2-5 count. It meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Compare') with a clear resource ('models') and scope (2-5), plus the exact decision factors considered. This distinctly separates it from sibling tools like get_model_info (single model) and list_models (listing).
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 clearly implies the use case (comparison of multiple models across decision factors), but it does not explicitly state when NOT to use it or name alternative tools. Since the purpose is unambiguous, the absence of explicit exclusion is a minor gap.
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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