Claude IPC MCP
The Claude IPC MCP server enables AI-to-AI communication across different instances and platforms, acting as a message broker with persistent message storage that survives restarts.
Register instances: Assign unique identifiers to AI instances for system identification
Send messages: Communicate directly with specific instances using text and optional structured data
Broadcast messages: Send messages to all active instances simultaneously
Check messages: Retrieve new messages sent to your instance
List instances: View all currently active and registered instances
Share files: Transfer file content between instances with optional descriptions
Share command outputs: Execute commands and share results with other instances
Rename instances: Change instance IDs (subject to rate limiting)
Auto-process messages: Automatically check and handle incoming messages
Cross-platform support: Enable interaction between various AI platforms like Claude, Gemini, and ChatGPT
Optional security: Support authentication for secure interactions
Allows Google Gemini AI to exchange messages with other AI assistants through both natural language commands and direct Python script execution.
Supports any Python-capable AI assistant to participate in the inter-AI communication network through script execution and natural language commands.
Mentioned as a use case where AI assistants can collaborate on React component development through message exchange.
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., "@Claude IPC MCPSend message to gemini: Can you review my latest code changes?"
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.
Claude IPC MCP - Let Your AI Agents Talk to Each Other
Enable AI-to-AI communication using simple natural language commands. Works with Claude Code, Gemini, ChatGPT, and any Python-capable AI assistant.
What It Does
Claude IPC MCP lets different AI assistants send messages to each other, even across different platforms and sessions. Think of it as email for AIs - simple, reliable, and persistent.
# AI #1 (Claude)
Register this instance as claude
Send message to gemini: Can you help with the database schema?
# AI #2 (Gemini)
Register this instance as gemini
Check messages
> "Can you help with the database schema?" - from claudeRelated MCP server: Command Executor MCP Server
Quick Install (2 minutes)
# 1. Clone the repo
git clone https://github.com/jdez427/claude-ipc-mcp.git
cd claude-ipc-mcp
# 2. Install UV package manager
curl -LsSf https://astral.sh/uv/install.sh | sh
# 3. Install dependencies
uv sync
# 4. For Claude Code: Run installer
./scripts/install-mcp.sh
# 5. Restart Claude Code and test
# Type: Register this instance as mynameFull installation guide: INSTALL.md
Key Features
💬 Natural language commands - No coding required
💾 Persistent messages - Messages survive restarts
🔄 Cross-platform - Works between different AI platforms
🎯 Simple setup - Install once, use everywhere
🔐 Optional security - Add authentication if needed
Basic Commands
Register this instance as alice # Set your name
Send message to bob: Hello! # Send a message
Check messages # Check inbox
List instances # See who's onlineDocumentation
INSTALL.md - Complete installation guide
TROUBLESHOOTING.md - Common issues and solutions
docs/ - Advanced features and platform-specific guides
Requirements
Python 3.12+ (check with
python3 --versionorpython --version)Any AI assistant with Python execution capability
Support
Having issues? Open a GitHub issue
License
MIT - Use freely in your projects
"Can't spell EMAIL without AI!" 📧
Available Tools
9 toolsauto_processC
Automatically check and process IPC messages (for use with auto-check feature)
| Name | Required | Description | Default |
|---|---|---|---|
| instance_id | Yes | Your instance ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral disclosure. It mentions 'automatically' and 'process' but doesn't explain what processing entails, whether it's destructive, what permissions are needed, or what happens to messages. The description is insufficient for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately brief with two concise phrases, though the parenthetical feels slightly tacked on. Every element serves a purpose, and there's no wasted verbiage.
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 tool that appears to perform automated message processing (implied mutation) with no annotations and no output schema, the description is inadequate. It doesn't explain what 'process' means, what happens to messages, what the tool returns, or any error conditions.
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% (the single parameter 'instance_id' is documented as 'Your instance ID'), so the baseline is 3. The description adds no additional parameter information beyond 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 states the tool 'Automatically check and process IPC messages' which provides a basic verb+resource combination, but it's vague about what 'process' entails and doesn't distinguish from siblings like 'check' or 'send'. The parenthetical 'for use with auto-check feature' adds some context but doesn't clarify the core purpose.
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 this tool versus alternatives like 'check' or 'send'. The parenthetical mention of 'auto-check feature' implies a specific context but doesn't define when this tool is appropriate versus manual checking/processing tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
broadcastC
Broadcast a message to all other Claude instances
| Name | Required | Description | Default |
|---|---|---|---|
| from_id | Yes | Your instance ID | |
| content | Yes | Message content | |
| data | No | Optional structured data |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but lacks critical details: whether this requires authentication, how messages are delivered (reliability, latency), if there are rate limits, whether it's idempotent, or what happens on failure. 'Broadcast' implies a write operation affecting multiple instances, but no safety or side-effect information is given.
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, clear sentence with zero wasted words. It's front-loaded with the core action and target, making it immediately scannable and efficient. Every word earns its place by conveying essential purpose 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?
For a tool with no annotations, no output schema, and a mutating action ('broadcast'), the description is incomplete. It doesn't explain what 'broadcast' entails operationally, what the response looks like, error conditions, or how it differs from siblings like 'send'. The agent lacks sufficient context to use this tool safely and effectively.
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 three parameters (from_id, content, data). The description adds no additional parameter semantics beyond implying 'content' is the broadcast message. This meets the baseline for high schema coverage but doesn't enhance understanding of parameter usage or constraints.
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 ('broadcast') and target ('to all other Claude instances'), making the purpose immediately understandable. It distinguishes this from point-to-point communication tools like 'send' in the sibling list. However, it doesn't specify what 'broadcast' entails technically (e.g., pub/sub, direct messaging).
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 is provided about when to use this tool versus alternatives like 'send' (point-to-point) or 'share_command' (command sharing). The description implies a broadcast scope but doesn't clarify use cases, prerequisites, or exclusions. The agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
checkC
Check for new messages
| Name | Required | Description | Default |
|---|---|---|---|
| instance_id | Yes | Your instance ID |
TDQS
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 but offers minimal insight. It mentions checking for 'new messages' but doesn't specify whether this is a read-only operation, how it interacts with the system (e.g., polling frequency, side effects), or what authentication or rate limits apply. The description fails to compensate for the lack of 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?
The description is extremely concise at just three words, with no wasted language. It is front-loaded with the core action ('Check'), making it easy to parse. Every word earns its place, though this conciseness comes at the cost of detail in other dimensions.
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 tool with no annotations, no output schema, and a single but critical parameter, the description is insufficient. It doesn't explain what 'new messages' means in context, what the tool returns, or how it differs from other message-related operations. The lack of behavioral and output information leaves significant gaps for an agent to understand and use the tool effectively.
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 100% description coverage, with the single parameter 'instance_id' clearly documented as 'Your instance ID'. The description adds no additional meaning beyond what the schema provides, such as explaining how the instance ID relates to message checking. Given the high schema coverage, the baseline score of 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 'Check for new messages' states a general action but lacks specificity about what resource is being checked. It doesn't distinguish this tool from potential siblings like 'list_instances' or 'auto_process' that might also involve message handling. The description is vague about scope and target, falling short of the clarity needed for effective tool selection.
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 is provided on when to use this tool versus alternatives. With siblings like 'list_instances', 'send', and 'auto_process', the description offers no context about whether this is for polling, notification checking, or status updates. The agent receives no help in determining the appropriate scenario for invoking this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_instancesB
List all active Claude instances
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'List' implies a read operation, it doesn't specify whether this requires authentication, what 'active' means operationally, if there are rate limits, or what format the output takes. The description is minimal and lacks important 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states exactly what the tool does with zero wasted words. It's appropriately sized for a simple listing tool and front-loads the core functionality.
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 listing tool with no output schema, the description is minimally adequate but lacks important context. It doesn't explain what constitutes an 'active' instance, whether the list is filtered or complete, or what information is returned about each instance. The absence of annotations means more behavioral detail would be helpful.
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?
With 0 parameters and 100% schema description coverage, the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema already fully documents the empty parameter set.
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 ('List') and resource ('all active Claude instances'), providing a specific purpose. However, it doesn't differentiate from sibling tools like 'check' or 'auto_process' that might also involve instance operations, so it doesn't fully distinguish from 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?
The description provides no guidance on when to use this tool versus alternatives like 'check' or 'register'. It doesn't mention prerequisites, exclusions, or appropriate contexts for listing instances versus other instance-related operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
registerC
Register this Claude instance with the IPC system
| Name | Required | Description | Default |
|---|---|---|---|
| instance_id | Yes | Unique identifier for this instance (e.g., 'wsl1', 'wsl2') |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'register' which implies a write/mutation operation, but doesn't specify whether this is idempotent, requires specific permissions, has side effects, or what happens on success/failure. This leaves significant gaps for a tool that likely modifies system state.
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, clear sentence that states the tool's purpose without unnecessary words. It's appropriately sized for a simple registration tool and front-loads the essential information efficiently.
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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what registration entails, what the expected outcome is, or any behavioral characteristics. Given the complexity of system registration and lack of structured data, more context is needed for the agent to use this effectively.
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 already documents the single parameter 'instance_id' with its description. The tool description adds no additional parameter information beyond what's in the schema, meeting the baseline for high schema coverage but not providing extra value.
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 ('register') and the target ('this Claude instance with the IPC system'), making the purpose understandable. It doesn't explicitly differentiate from siblings like 'list_instances' or 'rename', but the verb 'register' implies a distinct initialization action rather than listing or modifying.
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_instances' or 'check', nor does it mention prerequisites or context for registration. It states what the tool does but offers no usage context, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
renameA
Rename your instance ID (rate limited to once per hour)
| Name | Required | Description | Default |
|---|---|---|---|
| old_id | Yes | Your current instance ID | |
| new_id | Yes | The new instance ID you want |
TDQS
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 effectively adds context by specifying the rate limit ('once per hour'), which is a crucial behavioral trait not inferable from the input schema. However, it doesn't cover other aspects like permissions needed, whether the rename is reversible, or what happens on success/failure, leaving some 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 a single, efficient sentence that front-loads the key information ('Rename your instance ID') and adds a critical constraint ('rate limited to once per hour') without any wasted words. Every part of the sentence earns its place, making it highly concise and well-structured.
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 complexity (a mutation with no annotations and no output schema), the description is partially complete. It covers the core action and a rate limit but lacks details on prerequisites, error handling, or return values. For a tool that modifies an instance ID, more context on permissions or side effects would be beneficial, making it adequate but with clear gaps.
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 100% description coverage, with clear documentation for 'old_id' and 'new_id'. The description adds no additional meaning beyond what the schema provides, such as format constraints or examples. Given the high schema coverage, a baseline score of 3 is appropriate as the schema does the heavy lifting.
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 ('Rename') and the resource ('your instance ID'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'register' or 'list_instances', but the specificity of renaming an instance ID is sufficient for clarity without being tautological.
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 'register' (which might create a new instance) or other siblings. It mentions a rate limit, but this is more about behavioral constraints rather than usage context, leaving the agent with no explicit when/when-not instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sendC
Send a message to another Claude instance
| Name | Required | Description | Default |
|---|---|---|---|
| from_id | Yes | Your instance ID | |
| to_id | Yes | Target instance ID | |
| content | Yes | Message content | |
| data | No | Optional structured data to send |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't cover critical aspects like whether this is a synchronous or asynchronous operation, error conditions (e.g., if the target instance is unavailable), rate limits, or authentication requirements. This leaves significant gaps for an agent to understand how to use it effectively.
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, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a tool with a straightforward action, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of inter-instance communication and the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral traits, error handling, or return values, which are crucial for an agent to use this tool correctly in a real-world scenario.
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 already documents all parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as clarifying the format of 'instance ID' or the nature of 'structured data'. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('send') and resource ('a message to another Claude instance'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'broadcast' or 'share_command', which would require explicit comparison, but it's not vague or tautological.
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 'broadcast' or 'share_command'. It lacks context about prerequisites, such as whether instances need to be registered or online, and offers no explicit when-not-to-use or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
9 tool updates
- First observed
auto_process - First observed
broadcast - First observed
check - First observed
list_instances - First observed
register - First observed
rename - First observed
send - First observed
share_command - First observed
share_file
TDQS
Most tools have distinct purposes, but 'check' and 'auto_process' could cause some confusion as both relate to message processing. 'check' is for manual checking, while 'auto_process' is for automated checking with processing, but the boundary isn't perfectly clear. Other tools like 'broadcast', 'send', 'list_instances', and 'register' are clearly differentiated.
Tool names follow a consistent verb-based pattern (e.g., 'broadcast', 'check', 'list_instances', 'register', 'rename', 'send', 'share_command', 'share_file'), all using lowercase with underscores for multi-word names. The only minor deviation is 'auto_process', which includes a prefix, but overall the naming is highly consistent and readable.
With 9 tools, the count is well-scoped for an IPC system covering instance management, messaging, and file/command sharing. Each tool serves a clear purpose, such as registration, listing, sending messages, and sharing resources, making the set comprehensive without being bloated or sparse.
The tool set provides complete coverage for IPC operations: instance management ('register', 'list_instances', 'rename'), messaging ('send', 'broadcast', 'check', 'auto_process'), and resource sharing ('share_command', 'share_file'). There are no obvious gaps, enabling agents to handle the full lifecycle of IPC interactions effectively.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
MCP server for building and testing AI agents with multi-model experimentation and insights.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to control Unreal E…
The Telnyx MCP server is an official implementation of the Model Context Protocol that enables AI clients (like Claude Desktop, Cursor, and OpenAI Agents) to interact with Telnyx's telephony, messaging, and AI assistant APIs. It provides comprehensive capabilities including making and managing phone calls, sending SMS/MMS messages, purchasing and configuring phone numbers, creating AI assistants with custom instructions, managing cloud storage buckets, scraping and embedding website content, and handling integration secrets. The server exists as both a local implementation and a remotely hosted version, allowing developers to integrate real-world communication infrastructure directly into AI applications.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceA versatile Model Context Protocol server that enables AI assistants to manage calendars, track tasks, handle emails, search the web, and control smart home devices.23-
- AlicenseBqualityDmaintenanceA Model Context Protocol server that allows secure execution of pre-approved commands, enabling AI assistants to safely interact with the user's system.1822ISC
- AlicenseNot gradedqualityCmaintenanceA Model Context Protocol server that connects AI assistants with the Kernel platform, enabling them to deploy applications, automate web browsers, and manage cloud resources.33MIT
- AlicenseNot gradedqualityFmaintenanceA Model Context Protocol (MCP) server that enables multiple AI agents to share memory, coordinate tasks, and collaborate effectively across IDEs and CLI tools.3315MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/jdez427/claude-ipc-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server