ocr-mcp

by Prekzursil

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool serves a clearly distinct purpose: listing engines, OCR on single image, PDF, batch, cross-engine comparison, and accuracy evaluation. There is no overlap or ambiguity.
Naming Consistency4/5
Tool names follow a verb_noun pattern in snake_case, though some use 'ocr' as a verb prefix (ocr_image, ocr_pdf) while others use descriptive verbs (list_engines, compare_engines, evaluate_accuracy). This is mostly consistent and readable.
Tool Count5/5
Six tools is well-scoped for an OCR server, covering essential operations without bloat. Each tool earns its place.
Completeness4/5
Core OCR workflows are covered: single image, PDF, batch, engine listing, comparison, and accuracy evaluation. Minor gaps (e.g., no tool for engine configuration) but the surface is complete for typical use.
Average 4.3/5 across 6 of 6 tools scored. Lowest: 3.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavior. It explains the input format (glob or JSON list) and return format (JSON with count and results), which is essential. However, it does not disclose potential side effects, concurrency limitations, or error handling, leaving gaps in behavioral understanding.
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 extremely concise: two sentences with no wasted words. It front-loads the purpose ('OCR many images') and packs essential parameter and output format details into the second sentence.
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 tool with 3 parameters, 1 required, and no annotations, the description provides input/output format basics but omits engine options, language support, performance considerations, and error behavior. The output schema exists but the description still adds the return structure, making it adequate but not comprehensive.
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%, so the description should compensate. It explains the 'paths_or_glob' parameter format in detail but provides no semantics for 'engine' or 'lang' parameters, which have defaults but no description. Only one of three parameters is adequately documented.
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 starts with 'OCR many images,' which clearly states the tool's purpose as a batch OCR operation. It distinguishes itself from siblings like 'ocr_image' (single image) and 'ocr_pdf' (PDF).
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 multiple images via 'many images' and the glob/list input, but it does not explicitly guide when to use this tool versus alternatives like 'ocr_image' for single images or 'ocr_pdf' for PDFs. No when-not-to-use 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It discloses the return JSON structure in detail, including per-engine fields and consensus engine. It does not explicitly state read-only or destructive nature, but it's implied.
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 two sentences: first defines purpose, second details output. No unnecessary text. Slightly more could be added for parameter context, but it 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 the presence of an output schema (not shown), the description explains return values well. It does not cover error cases or prerequisites (e.g., engines must be available). Siblings provide context, but missing parameter info reduces completeness.
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 coverage is 0%, but the description adds no information about parameters. It does not explain 'path' or 'lang' beyond what the schema provides (name, type, required). This is a missed opportunity to clarify what image path is expected or language 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 uses specific verb-resource ('Run ALL available engines on one image and compare') and distinguishes from siblings like batch_ocr and ocr_image. It clearly states it's the core accuracy tool when no ground truth exists.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use ('when you have no ground truth'), implying contrast with evaluate_accuracy. However, it does not explicitly mention when not to use or list alternatives beyond the inferred context.
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 describes the output JSON format and the two input modes, but it doesn't address edge cases (e.g., what happens if both ocr_text and ocr_path are provided, or if files don't exist) or error behavior. Some ambiguity remains.
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 very concise with two focused paragraphs. It front-loads the core purpose, then systematically explains parameters and output. No unnecessary 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?
Given the tool's complexity and the presence of an output schema (though not shown in full), the description covers the return format and parameter semantics. It lacks details on error conditions and parameter exclusivity, but it is otherwise complete.
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 adds rich context to all five parameters: ground_truth_path is a UTF-8 text file, ocr_text is pre-extracted text, ocr_path triggers on-the-fly OCR, engine defaults to 'auto' and is used with ocr_path, and lang defaults to 'en'. This compensates for the 0% 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 explicitly states the tool scores OCR output using CER/WER metrics. It clearly distinguishes itself from sibling OCR tools by focusing on evaluation, not OCR extraction.
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 specifies when to use each input parameter (ocr_text vs ocr_path) and mentions the required ground_truth_path. However, it doesn't explicitly state when not to use this tool or mention alternatives among siblings, though the differentiation is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains the rasterization process, parameter defaults (DPI=300) and trade-offs (higher = slower, more accurate), and the structure of the JSON output. However, it omits potential error conditions or file size limits.
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 (5 lines) with a clear Args section and Returns section. Every sentence adds value; 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?
Given 5 parameters, no annotations, but a defined output, the description covers purpose, process, parameters, and return structure. It references ocr_image for engine/lang details. Minor gaps: no error handling or constraints on path validity, but generally complete.
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 adds full meaning for all five parameters: path (absolute path), engine/lang (refers to ocr_image), pages (range syntax), dpi (with default and performance trade-off). This significantly exceeds the schema's bare parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'OCR', the resource 'PDF', and the process 'rasterizing pages then running an engine per page'. This distinguishes it from siblings like ocr_image (single image) and batch_ocr.
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?
Usage is implied for PDFs, but there is no explicit guidance on when to use this tool versus alternatives like ocr_image or batch_ocr. No when-not-to-use or exclusions are provided.
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 tool's behavior well: it returns availability and status for each engine, and checks usability on the current machine. It does not cover error handling, but for a simple list tool this is sufficient.
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, no wasted words. The first sentence states the core purpose, the second adds output format and usage guidance. Highly efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and sibling tools, the description is complete. It explains the output structure and the tool's role in a workflow (called before compare_engines), which is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is 100%. The description adds meaning by explaining what the tool does and its output, which meets the baseline for zero-parameter tools.
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 lists OCR engines and their usability. It uses a specific verb ('List'), a resource ('OCR engines'), and distinguishes itself from siblings by mentioning compare_engines.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Call this first to see which engines compare_engines will actually run,' providing clear context for when to use. It lacks explicit when-not or alternatives, but the guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: return format (JSON with fields like engine, ok, text, mean_confidence, lines with confidence and bbox), preprocessing requirement of opencv, and language mapping per engine. No contradictions.
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 efficiently structured as a docstring with Args and Returns. Every sentence adds value without redundancy, front-loading the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description still provides a detailed breakdown of the return JSON, covering all parameters and behavioral nuances. Complete for a 4-parameter 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%, but the description compensates by explaining each parameter: path as absolute path, engine as list of options, lang as ISO code with per-engine mapping, preprocess as boolean. Adds significant 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 tool's purpose: 'OCR a single image file (PNG/JPG/TIFF/BMP).' This is a specific verb+resource combination that distinguishes it from sibling tools like ocr_pdf and batch_ocr.
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 explains parameters and options (e.g., engine choices, preprocessing) but does not explicitly state when to use this tool versus siblings. It implies usage context but lacks explicit alternatives.
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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