MCP-Saptiva

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: batch_embed and embed handle embeddings at different scales, chat and reason are for different types of model interactions, ocr is for image text extraction, list_models provides metadata, and help is for documentation. No overlap or ambiguity exists between these functions.
Naming Consistency5/5
All tools follow a consistent 'saptiva_' prefix with descriptive snake_case names (e.g., saptiva_chat, saptiva_embed, saptiva_ocr). This pattern is uniform across all 7 tools, making them predictable and easy to identify.
Tool Count5/5
With 7 tools, this server is well-scoped for an AI model service, covering core functionalities like chat, reasoning, embeddings, OCR, model listing, and help. Each tool earns its place without being overwhelming or insufficient for the domain.
Completeness4/5
The tool set covers key operations for an AI platform: chat, reasoning, embeddings (single and batch), OCR, model discovery, and help. A minor gap is the lack of tools for managing resources (e.g., deleting or updating models), but core workflows are well-supported.
Average 3.5/5 across 7 of 7 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
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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 provided, the description carries full burden for behavioral disclosure. It mentions model capabilities but doesn't describe authentication requirements, rate limits, costs, response formats, error handling, or whether this is a read/write operation. For a chat completion tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that efficiently convey core functionality and model options. It's front-loaded with the primary purpose and avoids unnecessary elaboration. Every sentence contributes value, though it could be slightly more structured with clearer separation of concepts.
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 chat completion tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what a 'chat completion request' entails, what the response format looks like, authentication requirements, or error conditions. The agent lacks crucial context for proper tool invocation despite good schema coverage.
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 5 parameters thoroughly. The description adds minimal value beyond the schema by mentioning model capabilities (fast, reasoning, tool-compatible) which slightly enhances understanding of the 'model' parameter options. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Send a chat completion request') and target resource ('to Saptiva AI models'), providing a specific verb+resource combination. It distinguishes from some siblings by focusing on chat completion rather than embedding, OCR, or model listing, though it doesn't explicitly differentiate from saptiva_reason which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. The description mentions model capabilities (fast, reasoning, tool-compatible) but doesn't specify use cases, prerequisites, or when to choose this over sibling tools like saptiva_reason. The agent receives no explicit when/when-not/alternatives information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states what the tool does, not behavioral traits like rate limits, error handling, performance characteristics, or output format. It mentions support for URLs and base64 encoding but lacks operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences that are front-loaded with the core purpose. Each sentence adds value: the first states the function, the second specifies input formats, and the third provides use cases.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., text format, confidence scores), error conditions, or limitations, leaving significant gaps for an agent to use it effectively.
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 both parameters thoroughly. The description adds minimal value by mentioning 'Supports both URLs and base64 encoded images' and use cases, but doesn't provide additional semantics beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Extract text from images using Saptiva OCR model' with specific examples of use cases (document processing, receipt scanning, image text extraction). It distinguishes from siblings by focusing on OCR rather than embedding, chat, or model listing, 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 usage for text extraction from images with examples, but doesn't provide explicit guidance on when to use this versus other OCR tools or alternatives within the Saptiva suite. No exclusions or prerequisites 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?
With no annotations provided, the description carries full burden. It states the tool lists models but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns paginated results, or if the data is cached. This leaves significant gaps for an agent to understand operational constraints.
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 efficiently conveys the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema), the description is adequate but incomplete. It lacks context on behavioral aspects like authentication or rate limits, which are important for operational use, especially with no annotations to fill those gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to add parameter information, and it appropriately doesn't mention any parameters, maintaining focus on the tool's purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('all available Saptiva AI models'), specifying what information is included (capabilities, descriptions, pricing). It doesn't differentiate from siblings like 'saptiva_help' which might also provide model information, so it falls short of a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention if this is for discovery, comparison, or selection purposes, nor does it reference sibling tools like 'saptiva_chat' or 'saptiva_embed' that might require model 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool generates embeddings and lists example applications, but it doesn't disclose key behavioral traits such as rate limits, authentication requirements, response format, or potential costs. For a tool with no annotation coverage, this leaves significant gaps in understanding how it operates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with two sentences: the first states the core purpose, and the second provides usage context. Every sentence adds value without redundancy, making it efficient and front-loaded for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and usage examples but lacks details on behavioral aspects like response format or limitations. Without annotations or an output schema, the description should do more to compensate, but it meets a minimum viable standard.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'text' documented as 'Text to convert to embedding vector.' The description adds no additional parameter semantics beyond what the schema provides, such as text length limits or encoding requirements. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate semantic embeddings for text using Saptiva Embed model.' It specifies the verb ('Generate'), resource ('semantic embeddings'), and technology ('Saptiva Embed model'). However, it doesn't explicitly distinguish this from sibling tools like saptiva_batch_embed, which appears to be a batch version of the same function.
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 provides implied usage guidance by stating 'Useful for similarity search, clustering, and RAG applications,' which suggests contexts where this tool is applicable. However, it doesn't explicitly state when to use this tool versus alternatives like saptiva_batch_embed or saptiva_chat, nor does it provide exclusions or prerequisites for use.
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 of behavioral disclosure. It clearly indicates this is a read-only, informational tool ('shows examples'), which implies no destructive actions or authentication requirements. However, it doesn't disclose potential behavioral traits like response format (likely text/markdown), whether it's static content or dynamically generated, any rate limits, or if it requires specific permissions. The description adds basic context but lacks depth for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a structured list of topics with brief explanations. Every sentence earns its place by directly supporting the tool's function as a beginner's guide. The use of emoji and clear headings enhances readability without adding fluff. It efficiently conveys necessary information in a compact format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, informational purpose) and no output schema, the description is moderately complete. It covers the purpose, usage context, and parameter semantics adequately. However, without annotations or output schema, it lacks details on behavioral aspects (e.g., response format, error handling) and doesn't fully address what the user can expect beyond 'examples'. For a simple help tool, this is acceptable but leaves some gaps in contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'topic' parameter fully documented via enum values and a description ('Tema de ayuda a mostrar'). The description adds value by listing and briefly explaining each enum option (e.g., 'quick_start: Inicio rápido con ejemplo básico'), providing semantic meaning beyond the schema's enum labels. This compensates well, though it doesn't detail parameter syntax or constraints beyond what's implied. With 1 parameter and high schema coverage, baseline is 3, but the added explanations justify a higher score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Muestra ejemplos de peticiones y respuestas de la API de Saptiva' (Shows examples of requests and responses for the Saptiva API). This is a specific verb ('shows examples') with a clear resource ('Saptiva API requests and responses'). It distinguishes from sibling tools like saptiva_chat or saptiva_ocr by focusing on documentation/examples rather than performing API operations. However, it doesn't explicitly contrast with saptiva_list_models (which might list tools vs. showing examples).
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 this tool: as a 'GUÍA PARA PRINCIPIANTES' (Beginner's Guide) to learn about API usage through examples. It lists specific topics available, which implicitly guides usage based on the user's learning needs (e.g., use 'quick_start' for basics, 'ocr_example' for image text extraction). However, it doesn't explicitly state when NOT to use it (e.g., for actual API operations) or name alternatives like official documentation or sibling tools for direct API calls.
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 provided, the description carries the full burden of behavioral disclosure. It adds useful context about the tool's behavior ('Shows the model's chain-of-thought reasoning process along with the final answer'), which is not covered by the schema. However, it lacks details on potential limitations, error handling, or performance traits (e.g., latency, rate limits), leaving gaps in 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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines without wasted words. Every sentence earns its place by providing essential information, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (reasoning tasks with multiple parameters) and the absence of annotations and output schema, the description is moderately complete. It covers the core functionality and use cases but lacks details on output format, error conditions, or advanced behavioral traits, which would be helpful for an AI agent to invoke it correctly.
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 parameters thoroughly. The description does not add any meaning beyond what the schema provides (e.g., it doesn't explain parameter interactions or provide examples). Baseline 3 is appropriate as the schema handles the heavy lifting, but no extra value is added.
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 purpose with specific verbs ('Use Saptiva Cortex for complex reasoning tasks') and distinguishes it from siblings by specifying its unique function (reasoning with chain-of-thought) versus embedding, chat, OCR, etc. It explicitly mentions the resource (Saptiva Cortex) and the action (reasoning tasks).
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 on when to use this tool ('Best for math, logic, analysis, and multi-step problems'), which helps differentiate it from alternatives like saptiva_chat or saptiva_embed. However, it does not explicitly state when NOT to use it or name specific sibling tools as alternatives, keeping it from a perfect score.
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 provided, the description carries the full burden of behavioral disclosure. It mentions the efficiency advantage over individual calls, which is useful context, but doesn't address other important behavioral aspects like rate limits, error handling, response format, or any constraints on batch size. The description adds some value but leaves 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with just two sentences that each earn their place: the first states the core functionality, and the second provides the key comparative advantage. There's zero wasted language and it's front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description should do more to compensate. While it clearly explains the purpose and when to use it, it doesn't describe what the embeddings look like, any limitations or constraints, or what happens in error cases. For a batch processing tool with no structured behavioral information, this leaves important 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?
The schema description coverage is 100%, with the single parameter 'texts' fully documented in the schema as 'Array of texts to convert to embeddings'. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline for high schema coverage without adding extra 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 specific action ('Generate embeddings for multiple texts at once') and the resource ('texts'), and explicitly distinguishes it from its sibling tool 'saptiva_embed' by noting it's more efficient for batch processing. This provides perfect clarity about what the tool does and how it differs from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('for multiple texts at once') and provides a clear alternative ('calling saptiva_embed multiple times'), with the efficiency comparison guiding the user toward this tool for batch scenarios. This gives complete guidance on tool selection.
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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