Glif
OfficialThe Glif MCP server enables you to interact with the Glif platform to run and manage AI workflows (glifs) and bots through the Model Context Protocol (MCP).
Run Glifs: Execute glifs with specific inputs using the
run_gliftoolAccess Metadata: Retrieve detailed information about glifs, including required inputs via
glif_infoDiscover Content: Explore curated selections with
list_featured_glifsor search by name/descriptionManage User Data: Access your published glifs, account details, liked glifs, and public runs
Customize Workflows: Save glifs as tools, manage bots, create custom tools, and access URI-based resources
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., "@Glifgenerate a meme about AI taking over the world"
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.
Glif MCP Server
Glif is a media-generation agent: generate images, video, and audio, transcribe, render HTML, search the web, run code, and chain multi-step media operations — from any MCP client.
Endpoint:
https://glif.app/api/mcp(Streamable HTTP, JSON-RPC 2.0)Auth: OAuth 2.1 with dynamic client registration — sign in with your Glif account, no API keys
Install page with one-click buttons: https://glif.app/mcp
Docs for agents: https://glif.app/llms.txt
This repo holds the registry metadata for the hosted server (see server.json); the server itself is part of the glif.app platform and is not open source.
Looking for the old locally-run stdio server (npm@glifxyz/glif-mcp-server)? It's deprecated — the code is parked on the legacy-local-server branch.
Tools
compose_project does the work — describe what you want in plain language, including a whole series of variations, and Glif picks the models and chains the steps. It returns a job_id immediately; poll get_job_status for the media, which comes back as resource_link blocks pointing at CDN URLs. The rest are read and upload helpers. Call tools/list for the authoritative set, or see https://glif.app/mcp.
Generation spends credits from the signed-in Glif account; read-only tools are free. See https://glif.app/pricing.
Related MCP server: ComfyUI MCP Server
Install
Claude (web / desktop)
Settings → Connectors → Add custom connector, paste:
https://glif.app/api/mcpClaude Code
claude mcp add --scope user --transport http glif "https://glif.app/api/mcp"Cursor
Or add to .cursor/mcp.json:
{ "mcpServers": { "glif": { "url": "https://glif.app/api/mcp" } } }VS Code
code --add-mcp '{"name":"glif","type":"http","url":"https://glif.app/api/mcp"}'ChatGPT
Enable developer mode, then Settings → Apps & Connectors → Add new connector, paste https://glif.app/api/mcp, pick OAuth.
Codex
codex mcp add glif --url "https://glif.app/api/mcp"
codex mcp login glifAny other MCP client
{ "mcpServers": { "glif": { "url": "https://glif.app/api/mcp", "transport": "http" } } }Your client opens a browser OAuth sign-in on first connect — approve it to link your Glif account. More clients (Replit, Hermes, OpenClaw, LM Studio, …) with copy-paste snippets: https://glif.app/mcp
Links
Website: https://glif.app
MCP install page: https://glif.app/mcp
Discord: https://discord.gg/glif
Registries
Anthropic MCP Registry
License
MIT - see LICENSE
Available Tools
6 toolslist_featured_workflowsARead-onlyInspect
Get a curated list of featured workflows (glifs) - AI-powered tools for generating images, text, and more.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description is consistent. However, no additional behavioral context (e.g., pagination, rate limits) is provided. The description adds no value beyond annotations.
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?
Single sentence, 17 words, immediately states action and resource. No unnecessary words.
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 read-only tool with no parameters and no output schema, the description adequately explains what it returns (curated list of featured workflows). However, it could mention output format or limitations 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?
Input schema has zero parameters with 100% coverage; baseline for zero parameters is 4. No parameter information needed.
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 verb 'get' and resource 'featured workflows (glifs)', and distinguishes from sibling tools like search_workflows (filtering) and my_workflows (user-specific).
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?
No explicit guidance on when to use vs alternatives. The description implies it's for browsing curated workflows, but does not mention when not to use or compare to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
my_user_infoARead-onlyInspect
Get detailed information about your Glif account, including profile info, recent workflows, and recent runs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, confirming no side effects. The description adds value by specifying returned data categories (profile, workflows, runs), going beyond the annotation. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, front-loaded sentence that efficiently communicates purpose and key return categories. No wasted words.
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 zero parameters and a simple read-only operation, the description adequately covers what the tool returns (profile, workflows, runs). No output schema, but the listing of categories provides sufficient context for an agent.
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?
No parameters in input schema (100% coverage by schema). Baseline is 4; description does not need to add parameter details. No additional semantics required.
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's purpose: retrieving detailed account info including profile, recent workflows, and recent runs. It distinguishes itself from siblings like my_workflows (which likely focuses on workflows alone) and search_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 usage for getting the current user's account details, but lacks explicit guidance on when to use versus siblings (e.g., when to use my_workflows instead for workflow-only data). No when-not or alternative suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
my_workflowsARead-onlyInspect
Get a list of your published workflows (glifs). Shows your AI workflows with run counts and creation dates.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint=true, which is consistent with the description. The description adds minor behavioral context (what data is shown) but does not cover potential issues like pagination or rate limits. No contradiction with annotations.
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 concise sentences with no fluff. The purpose is front-loaded, and the second sentence adds relevant details. 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?
Given no parameters and no output schema, the description adequately explains what the tool returns. It could mention pagination or ordering but is largely sufficient for a simple list 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 has no parameters, so schema coverage is 100%. The description adds value by clarifying that the list is scoped to the user's own workflows, which is not evident from the schema alone.
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 verb 'get a list', the resource 'your published workflows (glifs)', and specific details like 'run counts and creation dates'. It distinguishes from sibling tools such as list_featured_workflows and search_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 usage for personal workflows but does not explicitly state when to use this tool over alternatives like list_featured_workflows or search_workflows. No when-not or alternative naming is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_workflowAInspect
Run a workflow (glif) with the specified ID and inputs. Workflows can generate images, text, audio, and more. Inputs can include text, URLs, or base64-encoded media.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The ID of the workflow (glif) to run | |
| inputs | Yes | Array of input values. Can be text, media URLs, or base64-encoded media (data:image/png;base64,... or data:image/jpeg;base64,...) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-read-only and non-destructive behavior. The description adds that it generates media but does not disclose potential side effects, idempotency, or reliability characteristics.
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 concise with two sentences, front-loading purpose and adding relevant context about output types and input formats without redundancy.
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 description mentions potential output types but lacks details on return values, as there is no output schema. It also omits guidance on error handling or post-invocation steps, leaving gaps for an agent.
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 both parameters, and the description adds value by specifying acceptable input formats (text, URLs, base64-encoded media) beyond the array-of-strings type, aiding correct usage.
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 verb 'Run' and the resource 'workflow (glif)', specifying that workflows generate various outputs. This distinguishes it from sibling tools like list_featured_workflows and search_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 usage by stating its function but does not provide explicit guidance on when to use it versus alternatives. It lacks exclusion criteria or context about prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_workflowsARead-onlyInspect
Search for workflows (glifs) by name, description, or keywords. Find AI tools for image generation, text processing, and more.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query string |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint: true, which is consistent. The description adds that it searches by name, description, or keywords, but no additional behavioral details beyond what annotations provide.
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 purpose, no unnecessary words. Efficient and clear.
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 simple tool (one parameter, no output schema), the description adequately covers what it does and the types of results it returns. Could mention return format but not critical.
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% with one parameter. The description adds context that the query can be by name, description, or keywords, which adds meaning beyond the schema's 'Search query string'.
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 tool name and description clearly state the action (search) and resource (workflows/glifs). It distinguishes from sibling tools like list_featured_workflows by implying a general search across all 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 provides no guidance on when to use this tool versus alternatives like list_featured_workflows or my_workflows. No context on when to search vs list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
workflow_infoARead-onlyInspect
Get detailed information about a workflow (glif) including its input fields, recent runs, and creator info.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The ID of the workflow (glif) to show details for |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only (readOnlyHint: true). The description adds valuable detail about the return content (input fields, recent runs, creator info), making behavior transparent beyond the annotation.
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?
Single sentence that is concise, front-loaded with the action, and contains no extraneous words.
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 (one parameter, no output schema), the description provides sufficient context—what the tool does and what it returns—making it complete.
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 covers the single parameter 'id' fully (100% coverage). The description does not add additional meaning about the parameter beyond the schema, leading to baseline score.
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 it retrieves detailed information about a specific workflow, including inputs, runs, and creator. It distinguishes from siblings like list_featured_workflows (list) and run_workflow (execute), but does not explicitly contrast with alternatives.
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?
No guidance on when to use this tool versus siblings such as search_workflows or my_workflows. The description only explains the tool's function without providing use-case context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose: listing featured workflows, user info, personal workflows, running a workflow, searching workflows, and workflow details. No overlap or ambiguity.
Naming patterns are mixed: some use verb+noun (list_featured_workflows, run_workflow, search_workflows), some use possessive+noun (my_user_info, my_workflows), and one uses noun+noun (workflow_info). This inconsistency could confuse an agent.
6 tools is well-scoped for a workflow platform, covering essential operations without being overwhelming or too sparse.
Missing CRUD operations for workflows (create, update, delete), which are important for managing workflows. The set focuses on consumption and view only, leaving gaps for workflow creation and management.
Maintenance
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