Claude Agents Power

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but 'search-agents' and 'recommend-by-keywords' could cause some confusion as both involve finding agents based on keywords. However, their descriptions clarify that 'search-agents' is for direct keyword/name matching while 'recommend-by-keywords' is for project-based recommendations, which helps mitigate overlap.
Naming Consistency5/5
All tools follow a consistent verb_noun naming pattern with hyphens (e.g., 'analyze-project', 'list-agents', 'install-agents'). There are no deviations in style or convention, making the set highly predictable and readable.
Tool Count5/5
With 8 tools, this server is well-scoped for managing and discovering agents. The count is appropriate for the domain, providing essential operations like listing, searching, installing, and analyzing without being overwhelming or insufficient.
Completeness4/5
The tool set covers core workflows for agent management, including discovery (list, search, recommend), installation, and analysis. A minor gap is the lack of tools for updating or uninstalling agents, which could limit lifecycle management, but agents can still perform basic operations effectively.
Average 3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool analyzes and recommends, but doesn't describe what 'analyze' entails (e.g., file scanning, metadata extraction), what 'recommend' outputs (e.g., list of agents, scores), or any behavioral traits like performance, side effects, or limitations. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy for an agent to parse quickly. Every part of the sentence contributes to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of analysis and recommendation tasks, the description is incomplete. There's no output schema, and the description doesn't explain return values or behavioral details. With no annotations and minimal description, it fails to provide enough context for an agent to understand the tool's full behavior and output, especially compared to more detailed tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'projectPath' documented as 'Path to the project directory to analyze.' The description doesn't add any meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze a project directory and recommend suitable sub-agents.' It specifies the verb ('analyze'), resource ('project directory'), and outcome ('recommend suitable sub-agents'). However, it doesn't explicitly differentiate from sibling tools like 'recommend-by-keywords' or 'search-agents', which might also involve recommendations, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, and doesn't reference sibling tools like 'recommend-by-keywords' or 'search-agents' that might serve similar purposes. This leaves the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get detailed information' implies a read-only operation, it doesn't specify what 'detailed information' includes, whether authentication is required, potential rate limits, or error conditions. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's front-loaded with the core purpose ('Get detailed information'), making it easy to parse. Every word earns its place by conveying essential intent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' entails (e.g., metadata, capabilities, status), which is critical for a tool with no structured output definition. This leaves the agent guessing about the return value and usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters (agentName and language). The description doesn't add any parameter-specific details beyond what's in the schema, such as clarifying the format of 'detailed information' returned. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get detailed information') and resource ('about a specific agent'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'list-agents' or 'search-agents', which likely provide different types of agent information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this tool is appropriate (e.g., for detailed metadata vs. basic listings) or when to choose siblings like 'list-agents' (for summaries) or 'search-agents' (for filtering).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Get' implies a read-only operation, the description doesn't address key behavioral aspects like whether this requires authentication, what format the statistics are returned in, if there are rate limits, or how the data is aggregated. This leaves significant gaps for a tool that presumably accesses potentially sensitive usage data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single clear sentence that states the tool's purpose without any unnecessary words. It's perfectly front-loaded with the essential information, making it highly efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a statistics retrieval tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'download statistics' includes (e.g., counts, trends, time periods), how results are ordered, or what format the data is returned in. The agent would need to guess about the tool's behavior and output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the single 'limit' parameter. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get download statistics') and target resource ('for agents'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'get-agent-details' or 'list-agents' that might also provide agent-related information, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get-agent-details' or 'list-agents'. It doesn't mention any prerequisites, exclusions, or specific contexts where this tool is preferred over siblings, leaving the agent with insufficient usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action is 'install' but doesn't specify whether this is a write operation, requires permissions, has side effects like overwriting files, or involves network calls. This leaves critical behavioral traits undocumented for a tool that likely modifies a system.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy to grasp quickly without any wasted information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of installing agents (likely involving file system changes and network operations), no annotations, and no output schema, the description is insufficient. It lacks details on behavior, error handling, or what happens post-installation, making it incomplete for safe and effective use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what the input schema provides. Since schema description coverage is 100%, the baseline score is 3. The description doesn't elaborate on parameter meanings, such as what 'agentNames' represent or how 'targetPath' is validated, but the schema adequately covers these details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('install') and target ('multiple sub-agents to a project directory'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'refresh-agents' or 'list-agents', which could involve similar resources but different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing an existing project directory, or compare it to siblings like 'refresh-agents' for updates or 'list-agents' for viewing installed agents, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'recommends' agents, implying a read-only operation, but does not clarify if it requires authentication, has rate limits, returns structured data, or handles errors. The description lacks details on output format or any behavioral traits 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is front-loaded with the core purpose and appropriately sized for the tool's complexity. Every part of the description contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not address what the recommendations look like (e.g., list of agents, scores, details), how many results are returned, or any error conditions. For a recommendation tool with no structured output information, the description should provide more context to guide usage effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'keywords' parameter fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides (e.g., it does not explain keyword formatting, examples beyond the schema, or how recommendations are generated). Thus, it meets the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Recommend agents based on project keywords.' It specifies the verb ('recommend') and resource ('agents'), and indicates the input trigger ('project keywords'). However, it does not explicitly differentiate from sibling tools like 'search-agents' or 'list-agents,' which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention when to prefer it over siblings like 'search-agents' or 'list-agents,' nor does it specify prerequisites or exclusions. The usage context is implied but not articulated, leaving gaps for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool searches for agents but doesn't disclose behavioral traits such as whether it's read-only (likely, but not confirmed), how results are returned (e.g., pagination, format), or any limitations (e.g., rate limits, authentication needs). This is a significant gap for a search tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple search tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a search tool with 2 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of agents, their details), how results are structured, or any behavioral constraints. This leaves gaps for an AI agent to use the tool effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('query' for search and 'language' with enum values). The description adds minimal value beyond the schema by implying the 'query' parameter is for 'keyword or name' searching, but it doesn't provide additional syntax, format details, or context beyond what the schema specifies. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search for agents') and resource ('agents'), specifying it's by 'keyword or name'. It distinguishes from siblings like 'list-agents' (which presumably lists without search) and 'get-agent-details' (which gets details for a specific agent). However, it doesn't explicitly differentiate from 'recommend-by-keywords', which might be a more specific recommendation tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention when to prefer 'search-agents' over 'list-agents' (e.g., for filtered results) or 'recommend-by-keywords' (e.g., for recommendations vs. general search). The description implies usage for searching but lacks explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a list operation, implying read-only behavior, but doesn't cover aspects like pagination, rate limits, authentication requirements, or what 'available' means in context. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's front-loaded with the core purpose and appropriately sized for a simple list operation, making it highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (a filtered list operation) and high schema coverage, the description is minimally adequate. However, with no output schema and no annotations, it should ideally provide more context about return values or behavioral traits to be fully complete for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters ('category' and 'language') well-documented in the input schema. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline for adequate but not enhanced parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List all available agents' clearly states the action (list) and resource (agents), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'search-agents' or 'get-agent-details', which would require more specificity about scope or filtering capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search-agents' or 'get-agent-details'. There's no mention of prerequisites, context for filtering, or comparison with sibling tools, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Refresh agents from GitHub repository' implies a write/mutation operation (updating existing agents), but doesn't specify what gets updated, whether this requires authentication, what happens to existing configurations, or potential side effects. For a tool that likely modifies system state, this is insufficient 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized for a zero-parameter tool and gets straight to the point without unnecessary elaboration. Every word contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that likely performs mutations (refreshing/updating agents), the description is incomplete. With no annotations, no output schema, and minimal behavioral context, important information is missing: what 'refresh' entails, success/failure conditions, authentication requirements, and how this differs from installation. The description doesn't provide enough context for safe, effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description doesn't need to compensate for any parameter gaps. A baseline of 4 is appropriate since there are no parameters to explain beyond what the empty schema already indicates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Refresh') and resource ('agents from GitHub repository'), making the purpose understandable. However, it doesn't differentiate this tool from sibling tools like 'install-agents' or 'list-agents' - both could involve GitHub repository operations. The description lacks specificity about what 'refresh' means in this context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. With siblings like 'install-agents', 'list-agents', and 'get-agent-details', there's no indication of when refreshing is appropriate versus installing, listing, or getting details. The description offers no context about prerequisites, timing, or relationship to other operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

claude-agents-power-mcp-server MCP server

Copy to your README.md:

[![claude-agents-power-mcp-server MCP server](https://glama.ai/mcp/servers/hongsw/claude-agents-power-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/hongsw/claude-agents-power-mcp-server)

Score Badge

Copy to your README.md:

[![claude-agents-power-mcp-server MCP server](https://glama.ai/mcp/servers/hongsw/claude-agents-power-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/hongsw/claude-agents-power-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/hongsw/claude-agents-power-mcp-server'

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