AI Research MCP Server

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation3/5
Some tools have clear distinctions (e.g., generate_daily_summary vs. get_daily_papers), but there is notable overlap between get_trending_repos and search_github_repos, and between get_daily_papers and search_latest_papers, which could cause confusion. The descriptions help differentiate, but the boundaries are not entirely clear.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., generate_daily_summary, get_trending_models), with minor deviations like search_by_area (which uses 'by' instead of a direct noun). Overall, the naming is readable and predictable, though not perfectly uniform.
Tool Count4/5
With 8 tools, the count is reasonable for an AI research server, covering summary generation, data retrieval, and search functions. It is slightly on the higher side but well within a manageable scope, with each tool serving a distinct purpose in the domain.
Completeness3/5
The toolset covers key areas like summaries, trending items, and searches, but there are gaps in CRUD operations (e.g., no tools for saving, updating, or deleting research data) and limited coverage of non-Hugging Face/GitHub sources. It supports core workflows but may leave agents needing additional functionality for comprehensive research management.
Average 2.9/5 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'trending' but doesn't clarify behavioral traits like how results are sorted, rate limits, authentication needs, or what data is returned. This leaves significant gaps in understanding the tool's operation and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose. It's appropriately sized and front-loaded, with no wasted words, though it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage context, making it inadequate for an agent to fully understand how to invoke and interpret results from this search tool.
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, clearly documenting all 5 parameters. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or search logic. Baseline score of 3 is appropriate as the schema adequately covers parameter semantics without extra value from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool searches for trending AI/ML GitHub repositories, which provides a general purpose. However, it's vague about what 'trending' means (e.g., based on stars, recency, or other metrics) and doesn't clearly distinguish it from sibling tools like 'get_trending_repos' or 'search_by_area', leaving ambiguity in scope and differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. The description doesn't mention sibling tools, prerequisites, or specific contexts for application, such as comparing to 'get_trending_repos' for broader trending or 'search_by_area' for non-AI/ML searches, leaving the agent without clear 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 full burden. It mentions 'generate' and 'comprehensive', but doesn't disclose behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, what format the summary is in, or how it sources data. This is inadequate 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 front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action and scope concisely.
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 no annotations, no output schema, and a tool that likely aggregates data from multiple sources, the description is incomplete. It doesn't explain what 'comprehensive' entails, how the summary is structured, or what the output looks like, leaving significant gaps for an AI agent to use it 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?
Schema description coverage is 100%, so the schema fully documents the three boolean parameters. The description adds no parameter-specific information beyond implying the summary includes papers, repos, and models, which aligns with the schema. Baseline 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'generate' and resource 'comprehensive daily summary of AI research activity', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'generate_weekly_summary' or 'get_daily_papers', which would require mentioning time scope or comprehensiveness distinctions.
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. With siblings like 'get_daily_papers', 'get_trending_models', and 'search_by_area', there's no indication of whether this tool aggregates those or serves a different purpose, 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 mentions 'comprehensive' but doesn't specify what that entails, such as data sources, format, length, or processing time. It fails to address potential limitations like rate limits, authentication needs, or whether the operation is read-only or has side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It is appropriately sized and front-loaded, making it easy to understand at a glance.
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 generating a summary from multiple sources (papers, repos, models) and the lack of annotations and output schema, the description is insufficient. It doesn't explain what the summary includes, its format, or how it's generated, leaving significant gaps for an AI agent to understand the tool's behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema already documents all parameters (include_papers, include_repos, include_models) with clear descriptions. The tool description adds no additional parameter information beyond what's in the schema, which is acceptable but not additive, resulting in the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'generate' and resource 'weekly summary of AI research activity', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from its sibling 'generate_daily_summary' beyond the temporal difference, which is why it doesn't reach 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 like 'generate_daily_summary' or other search tools. It lacks context about prerequisites, timing, or scenarios where a weekly summary is preferred over daily or other methods.
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. It states the tool retrieves trending repositories but doesn't mention any behavioral traits such as rate limits, authentication needs, data freshness, or what the output format looks like. This is a significant gap for a tool with potential external API calls.
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 front-loads the core purpose without any wasted words. It's appropriately sized for a straightforward tool, 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.
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 the tool returns (e.g., list of repos with details), any limitations, or how it interacts with siblings. For a tool that likely involves external data fetching, more context is needed to guide effective 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 input schema has 100% description coverage, clearly documenting all three parameters with enums and defaults. The description adds no additional semantic context beyond implying filtering by AI/ML, which isn't reflected in the parameters. Baseline 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.
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') and resource ('trending AI/ML repositories on GitHub'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_github_repos' or 'get_trending_models', which could handle similar content, so it misses the top 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. With siblings like 'search_github_repos' and 'get_trending_models' available, there's no indication of context, exclusions, or prerequisites, leaving the agent to guess 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.
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 mentions the sources but doesn't describe key behaviors such as rate limits, authentication needs, pagination, error handling, or the format of returned results. For a search tool with multiple parameters and no output schema, this leaves significant gaps in understanding how the tool operates.
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 and scope without any redundant information. It's front-loaded with the core functionality and specifies the sources concisely, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral traits, output format, and usage context relative to siblings. While concise, it doesn't provide enough detail for an agent to fully understand how to invoke and interpret results from this tool 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?
Schema description coverage is 100%, so the input schema fully documents all four parameters (keywords, days, sources, max_results) with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, meeting 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 ('search for latest AI/ML research papers') and resources ('from multiple sources'), specifying the domains (arXiv, Papers with Code, Hugging Face). However, it doesn't explicitly differentiate from sibling tools like 'search_by_area' or 'get_daily_papers', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_by_area' or 'get_daily_papers'. It mentions the sources but doesn't explain why one would choose this tool over others, leaving the agent to infer usage context 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves 'featured' papers, implying a curated or filtered list, but doesn't explain criteria for 'featured', potential rate limits, authentication needs, or what happens if no papers are found. This leaves significant gaps in understanding 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized for a simple tool, with no wasted information.
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 (1 optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output format, which are needed for full completeness in the absence of annotations and output schema.
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 'days' parameter fully documented in the schema. The description doesn't add any parameter semantics beyond what the schema provides, such as clarifying 'today's' versus the 'days' parameter or detailing output format. Baseline 3 is appropriate as the schema handles the parameter documentation.
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') and resource ('today's featured AI papers from Hugging Face'), making the purpose understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'search_latest_papers' or 'search_by_area', which could also retrieve papers, so it doesn't achieve full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_latest_papers' for broader searches or 'generate_daily_summary' for summaries, nor does it specify contexts or exclusions for usage.
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 operation, it doesn't specify whether this requires authentication, has rate limits, returns paginated results, or provides error handling. For a tool fetching external data with no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a simple data-fetching tool and gets straight to the point.
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?
For a tool with 3 parameters, 100% schema coverage, and no output schema, the description provides basic purpose but lacks context about behavioral traits, usage scenarios, or output format. It's minimally adequate but leaves gaps that could hinder effective tool selection and invocation.
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 all parameters well-documented in the schema itself. The description adds no additional parameter context beyond what's already in the schema, so it meets the baseline expectation without adding extra value.
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') and resource ('trending AI models from Hugging Face'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'get_trending_repos' or 'search_by_area', which appear to be related but 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.
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. With siblings like 'get_trending_repos' and 'search_by_area' that might overlap in domain, there's no indication of when this specific tool is appropriate or what distinguishes it from other search/fetch tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but provides minimal behavioral context. It mentions searching papers and repos by area but doesn't disclose rate limits, authentication needs, result formats, pagination, or what happens with invalid areas. For a search tool with no annotation coverage, this leaves significant gaps in understanding its operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It could be slightly more structured by explicitly separating paper and repo aspects, but it avoids redundancy and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain result formats, error handling, or behavioral constraints like rate limits. The combination of missing annotations and lack of output schema means the description should provide more context about what the tool returns and how it behaves.
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 all parameters. The description adds no additional parameter semantics beyond implying the 'area' parameter accepts values like 'llm, vision, robotics, bioinfo', which is already covered in the schema. Baseline 3 is appropriate when 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 tool searches for papers and repositories by research area, with specific examples (llm, vision, robotics, bioinfo). It distinguishes from siblings like 'get_daily_papers' or 'search_github_repos' by combining both paper and repo search with area filtering. However, it doesn't explicitly contrast with 'search_latest_papers' or 'search_github_repos' beyond the area focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when searching by research area rather than other criteria, but doesn't explicitly state when to use this tool versus alternatives like 'search_latest_papers' or 'search_github_repos'. No guidance on prerequisites, exclusions, or specific scenarios is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

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

Card Badge

Copy to your README.md:

[![AI-Research-MCP MCP server](https://glama.ai/mcp/servers/nanyang12138/AI-Research-MCP/badges/card.svg)](https://glama.ai/mcp/servers/nanyang12138/AI-Research-MCP)

Score Badge

Copy to your README.md:

[![AI-Research-MCP MCP server](https://glama.ai/mcp/servers/nanyang12138/AI-Research-MCP/badges/score.svg)](https://glama.ai/mcp/servers/nanyang12138/AI-Research-MCP)

Latest Blog Posts

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

MCP directory API

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

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nanyang12138/AI-Research-MCP'

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