Provides tools for interacting with Roboflow's computer vision platform, including searching models and datasets, creating models from prompts, running inference (detection, segmentation, classification), managing workflows, and uploading images to projects.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Roboflow MCP Server create a model for detecting potholes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that gives AI coding agents programmatic access to Roboflow's computer vision platform. Provides 10 tools across 5 groups: Discovery, Rapid Creation, Inference, Workflows, and Utilities.
curl -fsSL https://raw.githubusercontent.com/eusef/Eusef_Roboflow_MCP/main/install.sh | bashThis clones the repo to ~/.roboflow/mcp-server, installs dependencies, builds, and prints the config snippet to add to Claude Code.
Related MCP server: MCP Server for n8n Integration
Node.js >= 20.0.0
A Roboflow API key (get one at https://app.roboflow.com/settings/api)
git clone https://github.com/eusef/Eusef_Roboflow_MCP.git
cd Eusef_Roboflow_MCP
npm install
npm run buildSet your Roboflow API key as an environment variable:
export ROBOFLOW_API_KEY="your_api_key_here"Optional environment variables:
Variable | Description | Default |
| Your Roboflow API key (required) | -- |
| Override the base API URL |
|
| Default workspace ID | API key owner's workspace |
Add this to your Claude Code MCP settings (~/.claude/settings.json or project-level .claude/settings.json):
{
"mcpServers": {
"roboflow": {
"command": "node",
"args": ["/absolute/path/to/Eusef_Roboflow_MCP/dist/index.js"],
"env": {
"ROBOFLOW_API_KEY": "your_api_key_here"
}
}
}
}Tool | Description |
| Check API connectivity and key validity |
| Search Universe for existing models and datasets |
| List projects in your workspace |
| List curated pre-trained APIs (OCR, people, PPE, etc.) |
Tool | Description |
| Create a model from a natural language prompt (no training data needed) |
Tool | Description |
| Run object detection or segmentation on an image |
| Run classification on an image |
Tool | Description |
| List available Workflows in a workspace |
| Execute a Workflow pipeline |
Tool | Description |
| Upload an image to a project dataset |
npm testWatch mode for TypeScript compilation:
npm run devStart the server directly:
npm startMIT
Check Roboflow API connectivity and key validity. Use as a first call to verify setup.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 conveys the core purpose (checking connectivity and key validity) but does not disclose what the tool returns, whether it has side effects, or how errors are reported. This is acceptable for a simple status check but leaves gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loaded with the action and purpose. Every word earns its place, with no redundancy or filler. It is ideally sized for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema tool, the description is fairly complete: it states what the tool does and when to use it. However, it could be slightly enhanced by mentioning the expected response format (e.g., success/failure status) to fully prepare the agent, but this is not critical given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty (0 parameters), so schema coverage is 100%. The description adds contextual meaning by indicating the tool's functionality (connectivity and key validity), which helps the agent understand that no parameters are needed and that the tool performs a configuration check. The baseline for 0 params is 4, and the description reinforces this without adding unnecessary detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Check' and clearly identifies the resource: 'Roboflow API connectivity and key validity'. This distinguishes the tool from sibling tools like search, project list, and inference run, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use as a first call to verify setup.' This provides clear contextual guidance and implies that it should be used before other Roboflow operations, effectively differentiating it from alternative actions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run classification inference on a Roboflow model. Returns top-K class predictions with confidence scores.
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Image as a URL, base64 string, or local file path | |
| top_k | No | Number of top predictions to return | |
| model_id | Yes | Classification model ID in format 'project/version' (e.g. 'cats-vs-dogs/2') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosing behavior. It mentions the return format (top-K predictions with confidence scores) but does not describe side effects, authentication requirements, or edge cases. The inference nature is implicitly non-destructive, but this is not stated explicitly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action, and contains no redundant or filler content. Every word contributes to understanding the tool's purpose and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 params, no output schema, no annotations), the description covers the essential purpose and return format. It lacks details like error handling or timeouts, but for an inference call this is reasonably complete. The sibling context is not explicitly addressed, but the classification-specific wording helps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description does not add significant meaning beyond the schema; it only references 'top-K' which is already documented in the top_k parameter. The description repeats what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs classification inference on a Roboflow model and returns top-K predictions with confidence scores. This specific verb+resource combination distinguishes it from sibling tools like roboflow_inference_run, which is generic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for classification models, but does not explicitly state when to prefer this over roboflow_inference_run or other alternatives. It provides no exclusion criteria or usage scenarios, leaving the agent to infer based on the word 'classification'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run object detection or segmentation inference on a Roboflow model. Accepts image as URL, base64, or local file path.
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Image as a URL, base64 string, or local file path | |
| format | No | Response format | json |
| overlap | No | Maximum overlap threshold for NMS (0-100) | |
| model_id | Yes | Model ID in format 'project/version' (e.g. 'hard-hat/3') | |
| confidence | No | Minimum confidence threshold (0-100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It only repeats the image input format from the schema and says 'run inference', but offers no insight into network calls, timeouts, error handling, return behavior, or side effects. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose and a short note on accepted input formats. No filler or redundant elaboration, every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a straightforward inference tool with no output schema, the description is adequate but not complete. It omits any details about expected response structure, failure modes, or model ID usage beyond what the schema states, though the complexity is low enough that this is still a reasonable baseline.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 five parameters. The description's note about image input formats duplicates the schema's image property and adds no semantic value beyond what the schema already provides, scoring at the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool runs object detection or segmentation inference on a Roboflow model, which is a specific verb+resource+task combo. It distinguishes from sibling tools like roboflow_inference_classify (classification) and roboflow_workflow_run (workflows).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (for object detection/segmentation inference) and implicitly differentiates from classification, but it does not explicitly name alternatives or state exclusions. It provides clear context but no direct 'use this instead of X' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List Roboflow's curated pre-trained APIs for common vision tasks (OCR, people detection, PPE, license plates, etc.). These are production-ready, zero-setup endpoints.
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | Filter pretrained models by task type | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden. It conveys a read-only list operation through the verb 'List,' but adds no details about response format, pagination, or any limitations. The 'production-ready, zero-setup' phrase describes the endpoints, not the tool's behavior, so it adds little transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the verb and resource. Every clause adds useful context with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one optional parameter and no output schema. The description sufficiently conveys the tool's purpose and scope. It could mention that it returns a list of models, but that is strongly implied by 'List.'
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully covers the single 'task' parameter, including enum values and a description. The tool description adds no extra parameter meaning, so the baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' with a clear resource: 'Roboflow's curated pre-trained APIs.' It also provides examples of vision tasks (OCR, people detection, PPE, license plates), distinguishing it from sibling tools that list user projects or search the universe.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving ready-made, zero-setup vision APIs, but it does not explicitly state when to prefer this over roboflow_universe_search or roboflow_project_list, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List projects in the authenticated Roboflow workspace.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | Workspace ID. Defaults to the API key owner's workspace. |
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 mentions authentication context but does not disclose return format, pagination, error behavior, or explicitly confirm it's a read-only operation. The minimal wording leaves significant behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no redundant information. It is front-loaded with the action and clearly states the object and scope, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one optional parameter, no nested objects, no output schema), but the description does not explain what the response contains or any list-related behavior like empty results or ordering. Given there is no output schema, the description is somewhat under-specified, though acceptable for a basic list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single 'workspace' parameter, so the schema already fully documents parameter meaning. The description adds no additional parameter-specific detail beyond reinforcing that the workspace is the authenticated one, which aligns with the schema's default note. Baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and a clear resource ('projects in the authenticated Roboflow workspace'). It unambiguously distinguishes this tool from siblings like roboflow_workflow_list or roboflow_pretrained_list by naming the exact object type and scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (to list projects in the user's own workspace) but does not explicitly state when not to use it or mention alternatives. The phrase 'authenticated workspace' hints at the contrast with universe search, but no explicit guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a model using Roboflow Rapid (prompt-based, no training data required). Describe what to detect in natural language.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Natural language description of what the model should detect or classify | |
| workspace | No | Workspace ID. Defaults to the API key owner's workspace. | |
| project_name | Yes | Name for the new project |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool is prompt-based and requires no training data, but it does not mention side effects, prerequisites, or whether the operation is asynchronous. This is partial transparency, adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise, and front-loaded with the core action. Every word adds value, with no redundant or irrelevant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity and the absence of an output schema, the description adequately conveys the primary purpose and input method. It lacks return value details, but that is less critical without an output schema and for a create operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers all parameters with descriptions (100% coverage). The description reiterates that the prompt is a natural language description but adds no new parameter semantics beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (create a model) and the specific method (Roboflow Rapid, prompt-based, no training data). It distinguishes this tool from siblings like inference or listing tools by emphasizing the creation capability and natural language input.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly implies when to use this tool: when you want to create a model quickly via natural language without training data. It provides clear context but does not explicitly mention alternative tools or exclusion scenarios, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Roboflow Universe for existing models and datasets. Use before building a custom model to check if a solution already exists.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Filter by resource type | |
| limit | No | Maximum number of results (1-20) | |
| query | Yes | Search query for Roboflow Universe |
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. 'Search' implies a read-only operation, but the description does not explicitly state side-effect freedom, result format, or pagination behavior. It adds context about usage but lacks deep behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, action-oriented, and front-loaded with the operation. It contains no filler and efficiently conveys purpose and usage context in minimal space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with fully documented parameters and a clear purpose, the description is sufficiently complete. It could explicitly mention the return format, but since there is no output schema, the current description still provides adequate context for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with descriptions for query, type, and limit. The description does not add any semantic detail beyond what the schema already provides, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Search') and resource ('Roboflow Universe'), explicitly names the objects ('models and datasets'), and distinguishes the tool from siblings like 'roboflow_pretrained_list' by focusing on public Universe search. It also states the intended use ('check if a solution already exists'), making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides a clear when-to-use condition: 'Use before building a custom model to check if a solution already exists.' This is explicit enough, though it does not name alternative tools or exclusion criteria, so it stops short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Upload an image to a Roboflow project dataset for training. Specify the dataset split (train/valid/test).
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Optional tag for the uploaded image | |
| image | Yes | Image as a URL, base64 string, or local file path | |
| split | No | Dataset split to upload into | train |
| workspace | No | Workspace ID. Defaults to the API key owner's workspace. | |
| project_id | Yes | Target project ID |
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 identifies the action as an upload but does not disclose side effects, authentication needs, rate limits, idempotency, or what happens on duplicate images. For a mutation tool, this is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the action. No filler or repetition. The split instruction is relevant and earns its place. Very efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters, no annotations, and no output schema, the description is minimally adequate but leaves gaps. It explains the core purpose and split, but omits behavioral details (auth, side effects) and does not mention what happens after upload. The schema covers parameter details, so this is not a severe gap, but it is not complete for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all parameters with descriptions (100% coverage), so the baseline is 3. The description only repeats the split parameter without adding deeper meaning (e.g., accepted image formats or behavior with the 'tag' parameter), so it doesn't elevate above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Upload') and resource ('image to a Roboflow project dataset'), clearly distinguishing it from sibling tools like 'roboflow_inference_run' or 'roboflow_project_list'. It also highlights the key split parameter, 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context is clear: this tool is for uploading images into a dataset split, as opposed to inference, listing, or workflow tools. It does not explicitly name alternatives or exclude non-training splits, but the context is sufficient for an agent to know when to select it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available Roboflow Workflows in a workspace.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | Workspace ID. Defaults to the API key owner's workspace. |
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. However, it only states the action 'List available...' without mentioning whether the operation is read-only, any authentication needs, or what the response contains. For a list operation, read-only behavior is implied, but the description adds no extra context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that directly states the tool's purpose. There is no fluff, redundant information, or unnecessary elaboration. It is front-loaded with the verb and resource, making it immediately scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one optional parameter and no output schema. The description explains the core action but does not specify what information the returned list contains (e.g., workflow names, IDs) or the response format. Given the simplicity and the fact that the schema covers the input, the description is adequate but could be slightly richer for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the single parameter `workspace` with a clear description ('Workspace ID. Defaults to the API key owner's workspace.'). The tool description does not mention this parameter or add any additional semantics. With 100% schema coverage, the baseline is 3, and the description provides no extra value in this dimension.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' and clearly identifies the resource as 'available Roboflow Workflows' with the scope 'in a workspace.' This effectively distinguishes it from sibling tools like roboflow_workflow_run (which executes workflows) and roboflow_project_list (which lists projects). The purpose is immediately obvious and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: it is used to list available workflows in a workspace. It implies the use case of inspecting existing workflows before running them, but it does not explicitly mention alternatives or exclusions (e.g., 'For running a workflow, use roboflow_workflow_run'). Since the context is evident and no exclusions are needed, this is slightly above average.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a Roboflow Workflow. Workflows chain multiple models and logic steps into a single pipeline.
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Image as a URL, base64 string, or local file path | |
| workspace | No | Workspace ID. Defaults to the API key owner's workspace. | |
| parameters | No | Additional workflow parameters | |
| workflow_id | Yes | Workflow ID to execute |
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 only says 'Execute' which implies a run operation, but it does not disclose side effects, required permissions, rate limits, or what the return value is. This is insufficient for a tool with no annotation guidance.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the verb, and contains no redundancy. Every word adds value, making it a model of conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has nested objects and no output schema, but the schema covers all parameters. The description is adequate for basic understanding but lacks details about return values or workflow execution nuances, which is a gap given the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond the schema; it only mentions the general concept of chaining models, not the specific parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute') and the resource ('a Roboflow Workflow'), and the second sentence defines what a workflow is ('chain multiple models and logic steps'), which distinguishes it from sibling tools like roboflow_workflow_list (listing) and roboflow_inference_run (single model inference).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (when you need to run a multi-step pipeline), but it does not explicitly state when not to use it or mention alternatives. Sibling tools like roboflow_inference_run exist for single-model inference, but no comparison is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0roboflow_api_statusroboflow_inference_classifyroboflow_inference_runroboflow_pretrained_listroboflow_project_listroboflow_rapid_createroboflow_universe_searchroboflow_upload_imageroboflow_workflow_listroboflow_workflow_runEach tool targets a distinct operation: status checking, searching, listing different resource types, creating via Rapid, running different inference types, executing workflows, and uploading images. The only potential overlap between inference_run and inference_classify is clearly differentiated by model task type.
All tools share the 'roboflow_' prefix, but the suffix pattern is inconsistent: some use noun_verb (universe_search, inference_run), some use noun_noun (project_list, api_status), and one uses verb_noun (upload_image). This mix of patterns makes it less predictable than a uniform convention.
With 10 tools, the server is well-scoped, covering the core Roboflow workflows without being bloated. Each tool serves a clear purpose and the count fits the intended functionality.
The set covers discovery, inference, workflow execution, and data upload, but lacks project management operations like create/update/delete project or dataset. This creates notable gaps for users who need to set up new projects outside of Rapid creation.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
A Model Context Protocol server for Wix AI tools
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
MCP server for building and testing AI agents with multi-model experimentation and insights.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/eusef/Eusef_Roboflow_MCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
