Mistral OCR MCP Server

by uuina

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes: health_check, list_pages, and API key management are clearly separate. The four OCR tools form two pairs (from_file vs from_url, and return string vs save to file), which are distinguishable by source and output, but could cause confusion if an agent overlooks the output difference.
Naming Consistency3/5
Naming uses a mix of patterns: 'ocr_from_file' vs 'ocr_to_markdown_file' use different prepositions for source vs destination, while 'set_api_key' and 'set_api_keys' are consistent. Overall, the names are readable but not perfectly consistent.
Tool Count5/5
8 tools cover the essential functions: health check, file listing, two OCR sources, two output options, and API key management. This is well-scoped for an OCR server without being excessive.
Completeness4/5
The tool set covers all core OCR operations (from file and URL, output as string or file) plus configuration and key management. Minor gaps exist, such as no direct model selection or cache clearing, but the main workflow is complete.
Average 3.5/5 across 8 of 8 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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?
No annotations are provided, so the description carries full burden. It only states the action but does not disclose side effects (e.g., overwriting, persistence), output format, or authentication requirements. Minimal behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it sacrifices necessary detail. It is not verbose, but the brevity reduces informativeness.
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 simplicity of the tool (one parameter, single action), the description is incomplete. It fails to explain behavioral nuances, differentiation from 'set_api_keys', or expected outputs, leaving gaps for an AI agent.
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 parameter 'api_key' has 0% schema description coverage and the tool description adds no extra meaning. It does not explain the expected format, length, or any constraints, making it unhelpful beyond the parameter name.
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 action (set) and the resource (one Mistral API key). It distinguishes itself from the sibling 'set_api_keys' tool by specifying 'one' key, implying a singular operation.
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 on when to use this tool versus its sibling 'set_api_keys'. The description does not mention prerequisites, context, or alternatives, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether keys are replaced or appended, whether the operation is reversible, or any security implications. The description only states the action without side effects or safety context.
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 very concise – one clear sentence plus an args section. Every sentence serves a purpose. It could be slightly improved by including behavioral details while remaining concise.
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 and the presence of an output schema, the description provides the basic purpose and parameter format. However, it lacks context about side effects (e.g., does it replace all keys?) and differentiation from the sibling, making it only minimally 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?
The schema coverage is 0%, so the description must compensate. It explains that 'api_keys' should be comma-, semicolon-, or newline-separated strings, adding format meaning beyond the schema. However, it does not specify validation, example values, or error handling, leaving gaps.
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 sets Mistral API keys for the MCP server. The name 'set_api_keys' implies setting multiple keys, and the description says 'one or more', which hints at the difference from sibling 'set_api_key'. However, it does not explicitly differentiate, so it lacks full sibling distinction.
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 the sibling 'set_api_key', nor are there prerequisites or context about the running server. The description is purely declarative without usage cues.
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 provided, so description must compensate. It mentions default model, image extraction default, and lists parameters but lacks details on side effects, auth, or rate limits.
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 structured with a brief introduction and parameter list, avoiding fluff. Could be more compact but is reasonably concise.
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 8 parameters and an output schema, the description covers parameter semantics adequately but misses usage context and output details (though output schema may compensate).
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 0%, but the description provides explanations for each parameter. However, there is a contradiction: description says default include_images=False, but schema shows default true, reducing reliability.
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 extracts Markdown text from a public URL, listing key parameters and defaults. However, it does not distinguish from sibling tools like ocr_from_file or ocr_to_markdown_file.
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 explicit guidance on when to use this tool vs alternatives (e.g., file-based OCR). The description only implies URL usage.
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?
Without annotations, the description carries the burden of behavioral disclosure. It indicates that PDF page count is conditional ('when available'), but does not detail permissions, side effects, or edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and key output, with no wasted words. It is appropriately concise for a simple tool.
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 and the presence of an output schema, the description is moderately complete for basic understanding, but lacks detail on parameter specifics and usage 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?
The single parameter 'file_path' has no schema description (0% coverage), and the description does not elaborate on expected format or constraints beyond implying it refers to a local file. This adds minimal value over the parameter name.
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 returns local file information and PDF page count when available. It distinguishes itself from sibling OCR tools which have 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 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 compared to siblings or alternative approaches. The description does not mention prerequisites or context for usage.
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. The description discloses default model ('mistral-ocr-latest'), image extraction behavior, caching, and parameter effects, offering moderate transparency. Missing details on file overwrite or error behavior.
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 structured with a brief summary followed by a well-organized parameter list. It is informative without excessive verbosity, though the parameter section could be slightly more concise.
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 an output schema exists (not shown), the description adequately covers core functionality and parameters. However, it lacks details on error handling, authentication, or disk behavior, which would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description compensates for 0% schema coverage by explaining each of the 10 parameters with type, default, and purpose in Chinese, providing clear meaning 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 action ('OCR') and resource ('公网 URL 指向的文件') and output ('生成的 Markdown 保存至磁盘'), distinguishing it from siblings like ocr_from_url which likely returns text.
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 default model and parameter details but does not explicitly guide when to use this tool over siblings like ocr_from_url or ocr_to_markdown_file. Usage is implied but not contrasted.
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 provided, so description must carry behavioral info. It states it checks multiple aspects, but does not describe side effects, error behavior, or guarantee read-only nature. Minimal but not misleading.
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?
Single sentence, front-loaded with action verb 'Check', lists key items. Efficient and no wasted words.
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?
With zero parameters and presence of output schema, description covers the tool's purpose. Sibling tools indicate it's a diagnostic tool. Could mention it's safe to run anytime, but not necessary for this simplicity.
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?
No parameters exist, so baseline is 4. Description adds no parameter info because none are needed; schema coverage is 100%.
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?
Description clearly states the tool checks MCP configuration, key validity, output directory, and model name. This differentiates it from siblings like list_pages and OCR tools 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
While no explicit when-to-use guidance is provided, the context of siblings (list_pages, OCR, set_api_key) implies it's for verifying setup before using other tools. However, lacking explicit alternatives or prerequisites.
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?
Given no annotations, the description carries the full burden. It discloses defaults (model, include_images=False), parameter behaviors (header/footer extraction, image limits), and output format (Markdown string). However, it lacks details on error handling, rate limits, or required permissions, leaving some 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 well-structured: primary action first, then defaults, then parameter list. It is slightly long but every sentence adds value. Could be tightened by grouping related parameters, but remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 8 parameters, 1 required, and an output schema (not shown), the description covers all parameters and core behavior. It does not explicitly differentiate from siblings or detail return structure beyond 'Markdown text', but context is adequate for a single tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must fully explain parameters. It does so thoroughly: each parameter is described with purpose, default, and allowed values (e.g., table_format options). This adds significant meaning beyond the bare schema, enabling correct usage.
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 extracts Markdown text from local files or images. It distinguishes from sibling tools like ocr_from_url by specifying 'local file' and 'absolute path', leaving no ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives (e.g., ocr_from_url for remote files). Usage context is implied by the tool name and file_path parameter, but no explicit guidance or exclusion criteria are provided.
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 bears full responsibility. It discloses default model and image extraction behavior, but lacks details on destructive actions, authentication, or potential errors. The use_cache behavior is mentioned, adding some transparency.
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 front-loaded with the primary purpose, then default model and use case, followed by a structured parameter list. It is appropriately sized for 10 parameters, but could be slightly more concise by grouping defaults.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (10 parameters, no annotations, output schema exists), the description covers purpose, use case, and all parameters thoroughly. It lacks error handling or prerequisites, but the output schema compensates for return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates fully by explaining each of the 10 parameters with examples and defaults (e.g., pages as range, output_dir as directory path). This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'OCR local files and save generated Markdown to disk', with a specific verb and resource. It distinguishes from siblings like ocr_from_file (returns text) and ocr_url_to_markdown_file (URL input) by specifying local files and disk output.
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 notes it is suitable for PDFs or long documents to avoid exceeding context windows, providing clear context. However, it does not explicitly exclude alternatives or mention 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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