clipboard-vision-mcp

by Capetlevrai

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation4/5
Tools are mostly distinct, but analyze_clipboard (general clipboard analyzer) overlaps with specialized clipboard tools like code_from_clipboard and describe_ui_from_clipboard, potentially confusing an agent about which to use for a broad request.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., analyze_chart, extract_text, describe_ui_from_clipboard), with predictable suffixes for clipboard variants.
Tool Count5/5
12 tools is a well-scoped count for an image analysis server covering both file and clipboard inputs for common specialized tasks (text, code, UI, errors, diagrams) plus general analysis.
Completeness4/5
The set covers most common image analysis needs, but lacks clipboard-specific tools for chart and diagram analysis (only file versions exist), leaving a minor gap that the general analyze_clipboard can fill but with less specificity.
Average 3.1/5 across 12 of 12 tools scored. Lowest: 2.3/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 5 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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It only states 'interpret,' offering no information about side effects, required permissions, or output nature. This is severely insufficient for safe invocation.
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, efficient but under-specified. It lacks crucial details that would warrant a longer description, so it is not excessively verbose, but its brevity compromises completeness. A 3 reflects adequate conciseness with room for improvement.
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 tool's simplicity (one parameter, no output schema) and the presence of similar siblings, the description fails to provide enough context for correct usage. It omits return behavior, typical use cases, and differentiation from closely related tools, making it incomplete.
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 covers the single parameter 'image_path' at 100%, but the tool description adds no additional context about accepted formats, file size limits, or path conventions. With high schema coverage, a baseline of 3 is appropriate as the description provides no extra value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies 'interpret a diagram image file,' which narrows the scope to diagrams, distinguishing it from siblings like analyze_chart or analyze_image. However, 'interpret' is vague and does not clarify the tool's exact function, such as extracting text, recognizing shapes, or providing a semantic summary.
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 usage guidance, such as when to use this tool versus alternatives like analyze_chart or analyze_image. It does not mention prerequisites, context, or when not to use it, leaving the agent to infer from sibling names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description does not disclose behavioral traits such as read-only status, required permissions, performance characteristics, or side effects. The description only states the action without any operational 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 concise, consisting of a single sentence that front-loads the purpose. It avoids unnecessary words, but the brevity leaves out important details.
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 tool's potential complexity (image analysis), the lack of output schema and minimal description make it incomplete. The agent does not know what the tool returns or any constraints (e.g., file size limits, supported formats).
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%, yet the description only reiterates that image_path is required and prompt is optional. It does not explain what valid values for image_path (e.g., file path vs URL) or how the prompt influences analysis. This adds minimal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Analyze' and the resource 'image file', making the purpose understandable. However, it does not differentiate from sibling tools like analyze_chart or code_from_screenshot, which also involve image analysis.
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 its siblings (e.g., analyze_chart, code_from_screenshot). There is no mention of prerequisites or context in which this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden of behavioral disclosure, yet it only states 'describe' without indicating output format, response limitations, or any behavioral traits. This is insufficient for an agent to understand what the tool does beyond the simple verb.
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 concise with one sentence, but it lacks structure and additional needed context. It is not verbose, but the conciseness comes at the cost of missing critical 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?
Given the simplicity and full schema coverage, the description is incomplete. It omits output details (e.g., format, content), fails to differentiate from siblings, and does not leverage the schema to provide richer context.
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% with a clear parameter description ('Absolute path to the image file'). The tool description adds no extra meaning beyond the schema, so it meets the baseline of 3 but does not elevate it.
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 verb 'describe' and the resource 'UI screenshot file'. However, it does not distinguish itself from sibling tools like analyze_image or describe_ui_from_clipboard, as it lacks specific scope or method differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. Siblings such as describe_ui_from_clipboard (clipboard input) and analyze_image (broader analysis) exist, but no usage context, prerequisites, or exclusions are given.
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 present, so the description must carry behavioral disclosure. It only states the action but does not describe side effects, output format, or if the operation is read-only.
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?
A single sentence with no extraneous content. It is efficient, though it could benefit from slightly more detail without being verbose.
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 simple tool (one parameter, no output schema), the description is too brief. It does not explain what 'analyze' entails (e.g., extract text, generate summary), leaving the agent uncertain about the output.
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% with a clear parameter description. The tool description adds no additional meaning beyond what the schema already provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The tool name and description clearly state it analyzes a chart image file. However, it does not differentiate from sibling tools like 'analyze_image' or 'describe_ui', which may have similar functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool over alternatives. The description lacks context about prerequisites 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, the description must disclose behavior. It only states 'diagnose' without indicating side effects (e.g., read-only), required permissions, or output format. This is insufficient for safe invocation.
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, front-loaded sentence with no redundancy. However, it could include more context without sacrificing conciseness.
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 one parameter and no output schema, the description lacks details on what 'diagnose' returns (e.g., error analysis, possible fix). It feels incomplete as an instruction.
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% and the description adds no additional meaning beyond the schema's 'Absolute path to the image file.' Baseline score applies.
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 (diagnose) and the resource (error screenshot file). It is distinct from sibling tools like 'diagnose_error_from_clipboard' which uses clipboard input, so purpose is clear but could specify what 'diagnose' entails.
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 vs alternatives such as 'diagnose_error_from_clipboard' or 'analyze_image'. The agent must infer from the parameter type.
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?
The description simply states 'OCR an image file' without disclosing behavioral traits such as supported image formats, file size limits, performance characteristics, or error handling. Since no annotations are provided, the description carries the full burden but adds minimal behavioral 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 extremely concise at four words, front-loading the core purpose. However, it could be improved by including a brief sentence on usage or output. Still, it avoids verbosity and earns its place.
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 tool's simplicity (one parameter, no output schema), the description could provide minimal context about the output (e.g., extracted text) or common use cases. The current description lacks completeness, leaving the agent unaware of what the tool returns or any constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage (the 'image_path' parameter is described as 'Absolute path to the image file.'). The description does not add any extra semantic value beyond the schema, but the schema itself is sufficient. According to the guidelines, high schema coverage sets a baseline of 3.
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?
Describes the tool as 'OCR an image file,' which clearly indicates the specific operation (OCR) and the resource (image file). However, it does not differentiate from siblings like 'extract_text_from_clipboard' or 'analyze_image,' which could cause confusion about which tool to use.
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 usage context is provided. The description does not specify when to use this file-based tool versus clipboard alternatives, nor does it mention any prerequisites (e.g., valid image format) or conditions for effective use.
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?
Lacks annotations. Description only says it diagnoses and proposes fixes, but does not disclose how it processes the clipboard (e.g., reads image, text), what types of errors, or any side effects.
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 conveying essential purpose clearly. No wasted words.
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?
Simple tool but no annotations or output schema. For a diagnostic tool that proposes fixes, more detail on return format or error scope would improve completeness.
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, so schema coverage is 100%. The description does not need to explain parameters, but it could hint that the input is read automatically from clipboard.
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?
Clearly states it diagnoses error screenshots from clipboard and proposes fixes. However, the sibling 'diagnose_error' likely performs similar analysis but without clipboard, and the description does not differentiate.
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 like 'diagnose_error' or other clipboard analysis tools. Implied usage when clipboard has an error screenshot, but no explicit 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 bears full responsibility for disclosing behavior. It does not mention that this likely uses OCR, supported image formats, accuracy limitations, or whether it returns textual code or a file path.
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 extremely concise (one sentence) and front-loaded with the key information. It is not verbose, though it could be expanded without losing efficiency.
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 (single parameter, no output schema), the description is minimally adequate but lacks details about return value (e.g., raw text or code snippet) and edge cases.
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% (image_path described adequately), but the description adds no additional meaning, such as the expected image format or path syntax. 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 uses a specific verb 'Extract' and resource 'code from a screenshot file', clearly stating the tool's function. It implicitly distinguishes from sibling tools like 'code_from_clipboard' by specifying the source as a 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?
The description provides no guidance on when to use this tool versus alternatives such as 'code_from_clipboard', 'extract_text', or 'analyze_image'. No context about prerequisites or exclusions is given.
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, the description must disclose all behavioral traits. It states the action and outcome but omits details about how the clipboard is accessed, potential error cases (e.g., no image in clipboard, unsupported format), and whether any side effects occur. The tool's behavior is partially transparent but lacks completeness.
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 of 7 words, extremely concise. It front-loads the primary action 'Extract code' and adds the key detail 'identifying the language'. While efficient, it is so brief that some context is missed, preventing a perfect score.
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 no parameters and no output schema, the description should at least clarify the input source (e.g., reads current clipboard content). It does not explicitly state that the tool reads from the clipboard automatically, though the name implies it. Sibling tools provide more context, making this one minimally complete but adequate for basic understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so the description need not add parameter meaning. Since there are 0 parameters, the description is not required to elaborate. The baseline of 4 is appropriate as it does not mislead or omit necessary parameter information.
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 code from a clipboard screenshot and identifies the language. It specifies the verb 'extract', the resource 'code from clipboard screenshot', and the additional outcome 'identifying the language'. This clearly distinguishes it from siblings like 'code_from_screenshot' which loads from file, and 'extract_text_from_clipboard' which is for general text.
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 such as 'code_from_screenshot' or 'analyze_clipboard'. It does not mention prerequisites, like having a valid screenshot in the clipboard, nor does it specify when not to use it. Usage is only implied.
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 provided. Description only states it describes, implying read-only, but does not disclose behavior for invalid clipboard content, side effects, or prerequisites.
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 of 10 words, directly conveys purpose with 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?
For a zero-parameter tool, description covers core purpose. Lacks output format or constraints, but adequate given 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?
Input schema has zero parameters, so schema coverage is 100%. Description adds no parameter info, but baseline for 0 params is 4.
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 verb 'describe', resource 'UI in a clipboard screenshot', and aspects 'layout, components, state'. It distinguishes from sibling tools like 'describe_ui' (without clipboard) and 'analyze_clipboard'.
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?
Implicitly indicates use when clipboard holds a UI screenshot, but no explicit when-not-to-use or alternatives. Siblings exist but not referenced.
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 provided, so description must convey all behavioral details. It only states the core function, omitting what happens if no image, OCR failure, or output format. For a tool with no annotations, this is insufficient.
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?
A single sentence efficiently conveys the full purpose with no redundancy. Every word earns its place.
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 no parameters or output schema, the description is largely complete. It explains input (clipboard image) and output (text). However, lacks details on error cases or prerequisites, which would be helpful for a standalone tool.
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 zero parameters with 100% schema coverage, so baseline is 4. The description adds no parameter info but is unnecessary. It clearly conveys that the clipboard image is the implicit input.
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 performs OCR on the clipboard image and returns text. The verb 'OCR' and resource 'image in clipboard' are specific, distinguishing it from siblings like 'extract_text' or 'code_from_clipboard'.
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 (when you have an image with text in clipboard), but lacks explicit when-not-to-use or alternatives. No mention of alternatives like 'extract_text' for other sources or 'analyze_clipboard' for different analysis.
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 provided, so description must disclose behavior but only states 'read and analyze'. It does not mention side effects, permissions, return type, or limitations.
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-loaded with primary action, no wasted words.
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?
No output schema, so description should hint at return type. It only says 'analyze it', leaving ambiguity about what the agent receives back. Adequate but not complete.
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 covers 100% of parameters, and description adds value by explaining that the default analysis is a description request and the prompt parameter overrides it.
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 reads and analyzes the clipboard image, with explicit example triggers like 'look at this' and 'what's in my clipboard'. This distinguishes it from siblings like 'code_from_clipboard' 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It specifies when to use (user says certain phrases or pastes screenshot without file path) and mentions optional prompt override. However, it does not explicitly exclude situations where sibling tools would be better.
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:

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

Score Badge

Copy to your README.md:

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

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