XAPIHub MCP Extension

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Each tool has a distinct and clearly defined purpose targeting different resources or actions within the XAPIHub domain. For example, get_accessed_organizations retrieves organizations, get_api_details fetches API specifics, and get_catalogues handles catalogues, with no overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, such as get_accessed_organizations, get_api_details, and search_projects. This uniformity makes the tool set predictable and easy to understand.
Tool Count5/5
With 7 tools, the server is well-scoped for its purpose of interacting with XAPIHub API resources like organizations, projects, catalogues, and users. Each tool serves a specific function without redundancy, fitting within the typical 3-15 tool range.
Completeness4/5
The tool set covers core read operations for organizations, projects, catalogues, users, and API details, including search and connection testing. A minor gap exists in write operations (e.g., create or update resources), but agents can still perform essential queries and validations effectively.
Average 3/5 across 7 of 7 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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 mentions 'Get API details' and 'filter endpoint', implying a read-only operation, but doesn't specify authentication needs, rate limits, error handling, or what the output looks like (e.g., JSON structure, pagination details). For a tool with 9 parameters and no annotations, this leaves significant gaps in understanding its behavior 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 sentence that efficiently conveys the core action and resource, with no wasted words. It's front-loaded with the main purpose, though it could be slightly more structured by separating key elements. Overall, it's appropriately sized and avoids redundancy.
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 (9 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, error cases, or behavioral traits like pagination handling (implied by 'page' and 'size' parameters but not described). For a tool that likely returns detailed API information, more context is needed to guide 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?
Schema description coverage is 100%, so the input schema fully documents all 9 parameters with descriptions. The description adds minimal value beyond the schema, mentioning 'specific catalogue collection' and 'filter endpoint', which loosely relates to parameters like 'catalogueId' and 'collectionId', but doesn't provide additional syntax, format details, or context for optional filters like 'creators' or 'collections'. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Get API details for a specific catalogue collection using filter endpoint from XAPIHub API', which provides a verb ('Get') and resource ('API details'), but it's vague about what 'API details' entails (e.g., metadata, endpoints, configurations) and doesn't clearly differentiate from sibling tools like 'get_catalogues' or 'search_projects'. It specifies the endpoint source ('XAPIHub API') but lacks specificity on the exact nature of the details retrieved.
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 mentions 'specific catalogue collection' and 'filter endpoint', but it doesn't indicate scenarios for use, exclusions, or comparisons to sibling tools such as 'get_catalogues' (which might list catalogues) or 'search_projects'. Usage is implied through parameter context but not stated clearly.
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 catalogues but doesn't describe whether this is a read-only operation, if it requires authentication, what the return format looks like, or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 appropriately sized and front-loaded, with zero wasted information, 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 'catalogues' are in this context, the response format, or any behavioral traits like error handling. For a tool with two required parameters and no structured output documentation, more context is needed to be fully helpful.
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 ('organizationId' and 'projectId') documented in the schema. The description mentions these parameters generically ('for a specific organization and project') but doesn't add meaningful semantics beyond what the schema already provides, such as format examples or contextual usage. 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 catalogues') and specifies the target resources ('for a specific organization and project from XAPIHub API'), which provides a specific verb+resource combination. However, it doesn't differentiate this tool from its siblings like 'get_accessed_organizations' or 'get_recent_accessed_projects', which also retrieve organizational/project data, so it doesn't fully distinguish itself from alternatives.
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_projects' or 'get_accessed_organizations'. It mentions the required parameters (organization and project) but doesn't explain prerequisites, exclusions, or contextual usage scenarios, leaving the agent with minimal 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 tool gets data but doesn't describe what 'recent' means (e.g., time frame, recency criteria), whether it's paginated, rate-limited, or requires specific permissions. This is inadequate for a read operation with zero annotation coverage, as key behavioral traits are missing.
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 without unnecessary words. It could be slightly more structured by explicitly mentioning the parameter, but it earns its place by being direct and avoiding redundancy.
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 'recent accessed projects' entails (e.g., definition of 'recent', project attributes returned), behavioral constraints, or error handling. For a tool with no structured metadata, this leaves significant gaps in understanding its operation and results.
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 the single parameter 'organizationId' fully documented in the schema. The description adds no additional parameter semantics beyond implying the organization context. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
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 ('recent accessed projects'), specifying the source ('from XAPIHub API') and scope ('for a specific organization'). It distinguishes from siblings like 'get_accessed_organizations' (which gets organizations) and 'search_projects' (which searches rather than gets recent accessed ones). However, it doesn't explicitly contrast with all siblings, preventing 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 'search_projects' or 'get_accessed_organizations'. It mentions the context ('for a specific organization') but doesn't clarify use cases, prerequisites, or exclusions. This leaves the agent with minimal direction for tool selection.
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 mentions 'comprehensive filtering and pagination options,' which hints at functionality, but fails to cover critical aspects like read/write nature, authentication requirements, rate limits, or error handling. This leaves significant gaps for a search tool with multiple parameters.
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 ('search for projects in an organization') and adds key features ('comprehensive filtering and pagination options') without redundancy. Every word contributes to understanding, making it appropriately concise.
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 (7 parameters, no output schema, no annotations), the description is insufficient. It lacks details on return format, error conditions, or behavioral traits, leaving the agent with incomplete context for effective use. The high parameter count and absence of structured support demand more descriptive depth.
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 7 parameters. The description adds minimal value by implying filtering and pagination, but doesn't explain parameter interactions or usage nuances beyond what's in the schema. 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 ('search for projects') and the target ('in an organization'), with additional context about filtering and pagination. It distinguishes from siblings like 'get_recent_accessed_projects' by emphasizing comprehensive search capabilities rather than recent access. However, it doesn't explicitly differentiate from other search-like tools, keeping it from 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 mentions 'comprehensive filtering and pagination options' but doesn't specify scenarios, prerequisites, or exclusions. Without annotations or explicit sibling comparisons, the agent lacks context for tool selection.
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. While 'Get' implies a read operation, it doesn't specify what 'recent' means (time window, count limit), whether authentication is required, rate limits, or what format the returned data takes. This leaves significant behavioral gaps for a 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a zero-parameter tool and front-loads the essential 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?
For a tool with no annotations, no output schema, and behavioral uncertainty around 'recent' and authentication requirements, the description is incomplete. It doesn't provide enough context about what the tool returns, how results are structured, or important behavioral constraints that would help an agent use it correctly.
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 baseline is 4. The description appropriately doesn't waste space discussing non-existent parameters, though it could have mentioned why no parameters are needed (e.g., 'returns all recently accessed organizations for the current user').
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 ('recent accessed organizations from XAPIHub API'), providing specific verb+resource information. However, it doesn't differentiate this tool from its sibling 'get_recent_accessed_projects' which suggests similar functionality for a different resource type.
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_recent_accessed_projects' or 'get_current_user'. There's no mention of context, prerequisites, or exclusions that would help an agent select between these similar retrieval 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool tests a connection, implying a read-only, non-destructive operation, but doesn't specify what 'test' entails (e.g., whether it performs an actual API call, returns status codes, or checks credentials). For a tool with zero annotation coverage, this is a significant gap in transparency about its behavior and potential 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 extremely concise—a single, clear sentence—with no wasted words. It front-loads the essential purpose without unnecessary elaboration, making it easy for an agent to parse quickly. This efficiency is ideal for a simple tool.
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 simplicity (0 parameters, no output schema, no annotations), the description is minimal but adequate for basic understanding. However, it lacks context about what the test involves (e.g., success/failure indicators, error handling) or how it fits with sibling tools. Without annotations or output schema, the description should provide more behavioral detail 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, meaning there are no parameters to document. The description doesn't add parameter details, which is appropriate here. A baseline score of 4 is given because the absence of parameters reduces the need for semantic explanation, and the description doesn't mislead about inputs.
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: 'Test connection to XAPIHub API'. It specifies the verb ('Test connection') and the target resource ('XAPIHub API'), which is more informative than just restating the name. However, it doesn't explicitly differentiate this tool from its siblings (like get_api_details), 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 doesn't mention prerequisites (e.g., authentication status), typical use cases (e.g., verifying API availability before other operations), or relationships to sibling tools (e.g., whether this should be called before get_api_details). This lack of context 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. It states it 'gets' data, implying a read-only operation, but doesn't disclose behavioral traits such as authentication requirements, rate limits, error handling, or what 'details' include (e.g., fields returned). 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, efficient sentence with zero waste. It front-loads the core action ('Get current user details') and includes necessary context ('from XAPIHub API'). Every word earns its place, making it appropriately sized for a simple tool.
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 simplicity (0 parameters, no output schema), the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, or usage context. With no annotations or output schema, the description should do more to compensate, but it meets a basic threshold for a straightforward read operation.
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 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics beyond the schema, so a baseline of 4 is appropriate as it doesn't introduce confusion or redundancy.
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 ('Get') and resource ('current user details'), specifying the source ('from XAPIHub API'). It distinguishes this from siblings like get_accessed_organizations or get_recent_accessed_projects by focusing on user details rather than organizations or projects. However, it doesn't explicitly differentiate from all siblings (e.g., get_api_details might also retrieve user-related API info).
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. The description doesn't mention prerequisites, context (e.g., authentication state), or comparisons to sibling tools like get_api_details or search_projects. It lacks explicit when/when-not instructions or named alternatives.
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:

[![mcp-cursor MCP server](https://glama.ai/mcp/servers/PamithWelikala123/mcp-cursor/badges/card.svg)](https://glama.ai/mcp/servers/PamithWelikala123/mcp-cursor)

Score Badge

Copy to your README.md:

[![mcp-cursor MCP server](https://glama.ai/mcp/servers/PamithWelikala123/mcp-cursor/badges/score.svg)](https://glama.ai/mcp/servers/PamithWelikala123/mcp-cursor)

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/PamithWelikala123/mcp-cursor'

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