Codex Vision MCP

by darkamenosa

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation4/5
Tools target distinct visual domains (charts, UI, diagrams, errors) with clear descriptions. The fallback analyze_image could overlap but is explicitly a catch-all. Only extract_text_from_screenshot and diagnose_error_screenshot have slight boundary ambiguity, but usage notes clarify.
Naming Consistency4/5
All names use lowercase snake_case and a verb_noun or adjective_noun pattern. Some verbs vary (analyze, diagnose, extract, ui_diff_check, ui_to_artifact, understand), but the structure is predictable and readable.
Tool Count5/5
8 tools is well-scoped for a vision analysis server. Each tool addresses a specific common use case without overcrowding. The count is appropriate for the domain.
Completeness4/5
The tool set covers major vision tasks: general image/video analysis, UI comparison, error screenshots, text extraction, diagram understanding, and artifact generation. Minor gaps like object detection exist, but the set is comprehensive for typical developer workflows.
Average 3.9/5 across 8 of 8 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits 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
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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that the tool compares and reports differences, which is consistent. It does not disclose additional traits like authorization needs or output format, but the core behavior is clear.
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 with no wasted words. The verb 'compare' is front-loaded. Every part is informative.
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 is minimal but covers the core function. With 4 parameters fully documented in schema and an output schema present (not needing return value explanation), it is adequate but could add more detail about how the comparison works or limitations.
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 parameters are already well-documented. The description does not add extra meaning beyond what the schema provides, meeting the baseline.
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 compares two UI screenshots and reports differences. It specifies the verb 'compare' and resources (expected/reference vs actual/current). However, it does not explicitly distinguish from sibling tools like diagnose_error_screenshot or analyze_image, but the purpose is specific enough.
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 alternatives (e.g., analyze_image, diagnose_error_screenshot). The description implies usage for UI diff checking but lacks explicit when-to-use or when-not-to-use instructions.
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?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the agent knows it is a safe read operation. The description adds no further behavioral context (e.g., output format, handling of image types). It does not contradict annotations.
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 a single sentence that front-loads the action ('Analyze technical diagrams'). It is concise but could benefit from structured categories or bullet points for clarity.
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 an output schema present, return values need not be described. However, the description lacks details on error conditions, input size limits, or prerequisite knowledge. It is adequate but not comprehensive for a complex diagram analysis 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 schema already documents each parameter thoroughly. The description adds minimal extra meaning beyond listing diagram types, which is also present in the diagram_type parameter description.
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 'Analyze' and clearly identifies the resource 'technical diagrams', listing multiple diagram types (architecture, flowcharts, UML, etc.), which effectively differentiates it from sibling tools like analyze_data_visualization or analyze_image.
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 technical diagrams but does not explicitly state when to use this tool over siblings or when not to use it. No exclusions or alternative tool references 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?
Annotations already declare readOnlyHint and destructiveHint, so agent knows it is safe. The description adds the output format but does not elaborate on behavioral traits like external API calls or processing time, which are hinted by openWorldHint.
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 purpose and output summary without wasted words. Every sentence adds value.
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?
Description covers input types and output summary. Given the openWorldHint and optional image_detail parameter, additional context about processing time or image requirements would improve completeness. Output schema exists, so return format is covered.
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 coverage is 100%, so the schema already documents all parameters. The description does not add further semantic guidance for parameters, so baseline score of 3 applies.
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 the specific verb 'Diagnose' targeting error-related screenshots, listing input types (error messages, stack traces, etc.) and output types (cause, fixes, prevention). It clearly distinguishes from sibling tools like analyze_image (general) and extract_text_from_screenshot (text extraction).
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 error screenshots but does not explicitly state when not to use it or reference alternative tools for non-error images. It lacks explicit when-to-use and when-not-to-use guidance.
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?
Annotations provide readOnlyHint=true and openWorldHint=true. The description adds contextual detail about extracting metrics, trends, anomalies, and recommendations, which aligns with read-only behavior. However, it does not disclose potential limitations (e.g., inability to extract specific data points) beyond what annotations imply. 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 a single sentence of 17 words, front-loaded with the key purpose. Every word is necessary, and there is no repetition or filler. It is concise and well-structured.
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 but mentioned), the description does not need to explain return values. It adequately covers the input and expected output type (insights). However, it could be slightly more specific about the scope (e.g., supporting static charts vs. interactive dashboards), but overall 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%, so the schema already documents all parameters well. The description does not add meaningful semantics beyond what the schema provides (e.g., it does not explain the 'analysis_focus' parameter or how 'image_detail' affects analysis). Baseline 3 is appropriate.
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 verb 'Analyze' and the resource 'charts, graphs, dashboards, and other data visualizations'. It distinguishes from siblings like 'analyze_image' (general) and 'understand_technical_diagram' (technical diagrams), making the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for data visualization analysis but lacks explicit guidance on when to use this tool vs alternatives (e.g., when to use 'analyze_image' instead). There is no mention of when not to use it or prerequisites, leaving the agent to infer context from the tool name and siblings.
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?
Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds minimal behavioral context beyond 'flexible visual understanding', but does not contradict annotations. With strong annotation coverage, description adds limited extra 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?
Two sentences, no fluff, directly communicates purpose and usage. Efficiently structured for quick comprehension.
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 general-purpose fallback tool with an output schema (context signal), the description is largely sufficient. Lacks detail on output format or edge cases, but adequate given its role.
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 coverage is 100%, so baseline is 3. Description does not elaborate on parameter details beyond what the schema already provides, so no added 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?
Description clearly states 'General-purpose image analysis' and positions itself as a fallback for cases not covered by specialized tools, effectively distinguishing from siblings.
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?
Explicitly advises using it as a fallback when specialized tools do not apply, providing clear usage context. Lacks explicit when-not-to-use examples but sibling list compensates.
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?
Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds 'faithful OCR' but no additional behavioral context beyond what annotations provide. No contradiction.
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 with no wasted words. Front-loaded with action and examples, followed by usage context. Excellent structure.
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?
With full parameter descriptions, output schema, and annotations, the description is complete. It explains tool purpose and usage, and the sibling tools cover other image-related tasks.
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?
All parameters have schema descriptions (100% coverage). Description itself adds no extra parameter meaning; examples are given but not linked to specific parameters. Baseline 3 is appropriate.
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 explicitly states the tool extracts text from screenshots, listing specific use cases like code, terminal output, logs, etc. It clearly distinguishes from sibling tools that analyze images or data visualizations.
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?
Includes explicit guidance when to use: 'when the user needs faithful OCR or text reconstruction from an image.' No explicit when-not, but sibling names imply other tools for different tasks, so it's 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?
Annotations already mark it as read-only and non-destructive. The description adds that it requires ffmpeg and samples frames, which are behavioral traits not in annotations. 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?
Two sentences are efficient and provide essential information without redundancy. The purpose is stated first, and the guidance is front-loaded.
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, the description covers the key aspects: purpose, external dependency, and usage alternatives. It is complete enough for an agent to understand when and how to use the 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 coverage is 100%, so the schema already documents all parameters. The description does not add any additional parameter semantics beyond what is in the schema, so it merits a baseline score of 3.
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 analyzes video by extracting sampled frames with ffmpeg and sending them to Codex vision analysis. It distinguishes itself from siblings like analyze_image by noting that for precise work, one should use those alternatives.
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 mentions the requirement of ffmpeg on PATH, and provides guidance: for precise video work, extract frames manually and use other tools. This helps the agent decide when not to use this tool, though it could be more explicit about when to use it.
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?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds context about output types and exclusions, but does not disclose additional behavioral traits like permissions, rate limits, or response details. With annotations present, this is adequate.
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 with no wasted words. The first sentence describes the core action, the second provides clear usage boundaries. Every sentence adds value.
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 4 parameters, 100% schema coverage, annotations, and an output schema, the description is sufficiently complete. It covers purpose, usage boundaries, and output types. Minor improvement could detail image_detail options, but schema already handles that.
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?
Schema coverage is 100%, so baseline is 3. The description adds semantic context by listing artifact types ('frontend code, AI prompts, design specifications, or natural-language descriptions'), which relates to the output_type parameter, enhancing understanding beyond the schema enum.
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 pair ('Convert UI screenshots into artifacts') and lists artifact types. It explicitly distinguishes from siblings by stating what not to use it for (OCR-only screenshots, error messages, diagrams, charts), making the purpose clear and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states 'Use only for UI screenshots' and 'Do not use for OCR-only screenshots, error messages, diagrams, or charts', providing clear when-to and when-not-to-use guidance. However, it does not explicitly name sibling tools as alternatives, which would be more comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![codex-vision-mcp MCP server](https://glama.ai/mcp/servers/darkamenosa/codex-vision-mcp/badges/card.svg)](https://glama.ai/mcp/servers/darkamenosa/codex-vision-mcp)

Score Badge

Copy to your README.md:

[![codex-vision-mcp MCP server](https://glama.ai/mcp/servers/darkamenosa/codex-vision-mcp/badges/score.svg)](https://glama.ai/mcp/servers/darkamenosa/codex-vision-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/darkamenosa/codex-vision-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server