MCP Screen Text

by ikhide

Overview Schema Related Servers Score Discussions

JavaScript

Local

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation2/5
Multiple tools have overlapping purposes that could cause confusion. For example, capture_application_screen and capture_screen_and_extract_text both handle application window screenshots, while capture_screen and capture_screen_and_extract_text both handle full-screen captures. The descriptions help somewhat, but the boundaries between tools are unclear, especially for agents trying to choose between similar options.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with clear verb_noun structures (e.g., capture_screen, extract_text, list_applications). There are no deviations in naming style, making the set predictable and easy to parse programmatically.
Tool Count4/5
With 5 tools, the count is well-scoped for a screen text capture domain, though it feels slightly thin. Each tool appears to serve a purpose, but the overlap suggests some tools could be consolidated, making the count borderline but still reasonable for the server's functionality.
Completeness3/5
The tool set covers core operations like capturing screens, extracting text, and listing applications, but there are notable gaps. For instance, there's no way to manage or delete captured images, and the OCR functionality is split across tools without clear update or configuration options, which could lead to agent workarounds for full lifecycle handling.
Average 3.1/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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

Behavior2/5
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 states what the tool does but lacks critical details: whether it requires permissions, how it handles multiple windows of the same application, what happens if the application isn't running, or the output format (e.g., file path, base64). This is inadequate for a tool that interacts with system applications.
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, clear sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple tool, making it easy for an agent to parse quickly.
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 complexity of capturing application screens (system interaction, potential permissions) and the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like error handling, output location, or dependencies, leaving significant gaps for an agent to use 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 fully documents both parameters (applicationName and format). The description adds no additional parameter semantics beyond what's in the schema, such as examples of valid application names or implications of format choice. Baseline 3 is appropriate when the schema does all the work.
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 with a specific verb ('captures') and resource ('screenshot of a specific application window'), which distinguishes it from generic screen capture tools. However, it doesn't explicitly differentiate from sibling tools like 'capture_screen' or 'capture_screen_and_extract_text' beyond implying application-specific targeting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'capture_screen' (full screen) or 'capture_screen_and_extract_text' (which includes OCR). It mentions 'specific application window' but doesn't clarify prerequisites (e.g., application must be running) or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't mention permissions needed, whether it requires user interaction, file storage behavior, error conditions, or rate limits. For a screen capture tool, this leaves critical operational details unspecified.
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, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized for this simple tool and front-loads the essential information.
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 insufficient. It doesn't explain what the tool returns (e.g., file path, base64 data, error handling) or provide behavioral context needed for safe invocation. The agent would be operating with significant unknowns about this system interaction 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 fully documents both parameters. The description doesn't add any parameter meaning beyond what's in the schema (e.g., it doesn't explain what 'display number' means beyond the schema's '0 for primary display' or provide context for format selection). Baseline 3 is appropriate when schema does all the work.
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 ('Captures') and resource ('screenshot of the specified display'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'capture_application_screen' or 'capture_screen_and_extract_text', which would require mentioning this captures the entire display rather than a specific application window.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'capture_application_screen' or 'capture_screen_and_extract_text'. It doesn't mention prerequisites, exclusions, or comparative contexts, leaving the agent to guess based on tool names alone.
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 the full burden of behavioral disclosure. It states the core function but lacks details on traits such as performance expectations (e.g., speed, accuracy), error handling (e.g., invalid image formats), or output format (since no output schema exists). This leaves significant gaps for an agent to understand how the tool behaves beyond basic OCR.
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, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose ('Extracts text from an image using OCR'), making it highly concise and well-structured for quick understanding.
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 complexity of an OCR tool with no annotations and no output schema, the description is incomplete. It doesn't address key contextual aspects like what the output looks like (e.g., plain text, structured data), potential errors, or performance constraints. This makes it inadequate for an agent to fully grasp the tool's behavior and usage.
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 clear documentation for both parameters (imagePath and language). The description adds no additional meaning beyond what the schema provides, such as explaining OCR limitations or language code nuances. This meets the baseline score of 3, as the schema adequately covers parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('extracts text') and resource ('from an image using OCR'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'capture_screen_and_extract_text', which suggests a similar function but with additional capture steps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an image file), exclusions, or comparisons to sibling tools like 'capture_screen_and_extract_text', leaving the agent to infer usage context.
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 states the tool lists applications, implying a read-only operation, but doesn't specify details like whether it requires permissions, how it handles errors, or what the output format looks like. This leaves significant gaps 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 a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 parameters, no annotations, and no output schema, the description is minimally adequate. It explains what the tool does but lacks details on behavioral traits, usage context, or output format, which are important for a tool with zero structured data support.
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, and the schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline expectation for such cases, though it could theoretically note the absence of parameters more explicitly.
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 ('Lists') and the target resource ('all running applications that can be captured'), which provides a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from its siblings like 'capture_application_screen' or 'extract_text', which reduces it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or comparisons to sibling tools like 'capture_application_screen' or 'extract_text', leaving the agent with minimal direction.
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. While it describes the core operation, it lacks important behavioral details such as permissions needed, whether it requires user interaction, potential rate limits, error conditions, or what the output looks like (text format, confidence scores). For a tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, and the second provides essential usage context about capture scope. No wasted words or redundancy.
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 complexity of a combined capture+OCR operation with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (text format, errors, or structure), system requirements, or behavioral constraints. For a tool with this functionality and zero structured coverage beyond inputs, more context is needed.
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 fully documents all three parameters. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain the interaction between 'display' and 'applicationName' beyond what the schema already states). Baseline 3 is appropriate when the schema does the heavy lifting.
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 ('captures' and 'extracts') and resources ('screenshot' and 'text'), distinguishing it from siblings like 'capture_screen' (only captures) and 'extract_text' (only extracts). It explicitly mentions the combined operation in one step.
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 (for combined screenshot and text extraction) and distinguishes between full screen vs. application window capture. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., 'use capture_screen if you only need a screenshot').
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:

[![screen-capture-mcp MCP server](https://glama.ai/mcp/servers/ikhide/screen-capture-mcp/badges/card.svg)](https://glama.ai/mcp/servers/ikhide/screen-capture-mcp)

Score Badge

Copy to your README.md:

[![screen-capture-mcp MCP server](https://glama.ai/mcp/servers/ikhide/screen-capture-mcp/badges/score.svg)](https://glama.ai/mcp/servers/ikhide/screen-capture-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/ikhide/screen-capture-mcp'

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