Provides real-time access to package metadata for Flask, including latest versions and weekly download counts via the PyPI registry.
Allows monitoring the operational and service health status of GitHub.
Provides tools for querying real-time npm package data, including latest versions, weekly download counts, licenses, and dependency information.
Enables tracking of Python packages on PyPI, offering tools to check versions, download statistics, and package metadata.
Allows checking the latest version, license, and weekly download statistics for the React package on npm.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@grounded-mcp What's the latest version of React?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for real-time package versions, download counts, and service status — powered by Grounded.
Tracks 1,000+ npm and PyPI packages and 76 cloud services. No signup required.
claude mcp add grounded -- uvx grounded-mcpAdd to your claude_desktop_config.json:
{
"mcpServers": {
"grounded": {
"command": "uvx",
"args": ["grounded-mcp"]
}
}
}Add to your MCP settings:
{
"grounded": {
"command": "uvx",
"args": ["grounded-mcp"]
}
}Related MCP server: API Status Check MCP Server
Tool | Description | Example |
| Latest version of an npm or PyPI package |
|
| Weekly download count |
|
| Cloud service operational status |
|
| General-purpose fact lookup |
|
For npm/PyPI packages: latest_version, license, deprecated, dep_count, required_runtime, weekly_downloads
For services: status
Works out of the box with no API key (50 lookups/day per IP).
For higher limits, set GROUNDED_API_KEY:
claude mcp add grounded -e GROUNDED_API_KEY=your_key -- uvx grounded-mcpSign up for a free API key (1,000 req/day) at https://grounded-api.dev.
Ask your AI assistant:
"What's the latest version of React?"
The assistant calls check_package_version("react") and gets:
Value: 19.2.4
Source: https://registry.npmjs.org/react
Fetched at: 2026-03-06T12:00:00Z
Hash: sha256:a1b2c3...
Tier: warm (TTL: 300s)Check the weekly download count of a package on npm or PyPI.
Args: package_name: The package name — e.g. "react", "express", "flask", "requests" registry: "npm" or "pypi" (defaults to "npm")
| Name | Required | Description | Default |
|---|---|---|---|
| package_name | Yes | ||
| registry | No | npm |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It states it checks weekly download count, but does not disclose rate limits, caching behavior, or error handling. Adequate for a simple read operation but leaves some traits implicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with no fluff. Purpose stated upfront, parameter explanations follow immediately. Each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with few parameters and an output schema, description covers purpose and parameters well. Could mention that it returns weekly data, but output schema likely clarifies. Good enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It adds meaning with examples for package_name and clarifies registry values and default. Lacks validation details but adds significant value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it checks weekly download count for a package on npm or PyPI, with specific verb and resource. It distinguishes from siblings like check_package_version (checks version) or check_service_status (checks service status).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance, but the examples and registry options imply usage for download statistics. Could be improved by mentioning alternatives like check_package_version for version info.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check the latest version of a package on npm or PyPI.
Args: package_name: The package name — e.g. "react", "express", "flask", "requests" registry: "npm" or "pypi" (defaults to "npm")
| Name | Required | Description | Default |
|---|---|---|---|
| package_name | Yes | ||
| registry | No | npm |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It only states the action without disclosing behavior like data source, rate limits, or error handling. This leaves transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: a single sentence followed by a brief docstring. It front-loads the primary action and avoids any redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description covers the core inputs. However, it does not mention output format or potential errors, though an output schema exists. It is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by providing examples for package_name and enumerating registry values ('npm' or 'pypi'). This adds meaningful context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it checks the latest version of a package on npm or PyPI, specifying the verb 'check' and the resource 'latest version of a package'. It distinguishes from siblings like check_downloads (download counts) and check_service_status (service health).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (checking package versions on npm/PyPI) but does not explicitly state when not to use or provide alternatives. The sibling tools are distinct, so no confusion arises.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check the current status of a cloud service (via its status page).
Args: service_name: The service name — e.g. "github", "stripe", "openai", "cloudflare", "vercel"
| Name | Required | Description | Default |
|---|---|---|---|
| service_name | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 'via its status page' but lacks details about the mechanism (e.g., HTTP request, possible delays, caching) or what the returned status looks like. For a check tool, more transparency about output format or potential errors would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise: two sentences with purpose and parameter description. No extraneous words; front-loaded with the action. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, clear purpose) and the presence of an output schema, the description is largely complete. It could optionally describe the return type, but the output schema likely covers that. The description adequately supports an agent in using the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter (service_name), and schema coverage is 0%. The description adds value by listing example service names, which clarifies valid inputs beyond the schema's type string. However, it does not specify format requirements (e.g., case sensitivity or exact naming).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool's purpose: checking the current status of a cloud service via its status page. The verb 'Check' and resource 'status of a cloud service' are specific. It is distinct from sibling tools that deal with downloads, package versions, or facts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides concrete examples of service names (e.g., 'github', 'stripe'). Implies when to use: when you need to verify service health. Does not explicitly state when not to use or contrast with alternatives, but the clear purpose and examples offer sufficient guidance for this simple tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up any fact tracked by Grounded.
Args: source: The fact source — "npm", "pypi", or "statuspage" entity: The entity name — e.g. "react", "flask", "github" field: The fact field — e.g. "latest_version", "license", "deprecated", "dep_count", "required_runtime", "weekly_downloads", or "status"
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | ||
| entity | Yes | ||
| field | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only says 'look up', implying a read operation, but does not disclose behavioral traits like idempotency, rate limits, or authentication requirements. Lacks transparency for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise, with a clear main sentence followed by structured argument list. No unnecessary words. Information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup with 3 parameters and an output schema (though not detailed), the description is mostly complete. It covers the inputs well, but lacks any example output or confirmation of the return format. Given no annotations, it could be slightly more detailed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description provides examples and enumerates possible values for each parameter (e.g., source: 'npm', 'pypi', 'statuspage'; field: 'latest_version', 'license', etc.), adding significant meaning beyond the bare schema titles. However, it does not cover all parameters exhaustively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it looks up facts tracked by Grounded, which is a specific verb-resource combination. However, it does not differentiate from siblings like check_downloads or check_package_version, which are more specialized. The purpose is clear but could be more explicit about scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. There is no mention of when-not to use it or any context for selection among siblings. The description only explains parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.1check_downloadscheck_package_versioncheck_service_statuslookup_factThe tools `check_downloads` and `check_package_version` are both specific to package registries, and `lookup_fact` can replicate their functionality by querying the same sources (npm/pypi) with appropriate fields, causing ambiguity. `check_service_status` is distinct but also could be covered by `lookup_fact` with source 'statuspage'.
Three tools use the `check_` prefix, while `lookup_fact` uses a different verb but still follows a verb_noun pattern. The naming is mostly consistent and predictable.
With 4 tools, the set is small but reasonable for checking package metrics and service status. The redundancy suggests a slightly lower count could suffice, but overall it is not excessive or insufficient.
The server covers basic checks for npm/PyPI packages and cloud services, but lacks operations like listing available packages/services, comparing versions, or retrieving more detailed metadata beyond the predefined fields. Some intended functionality is indirectly available via `lookup_fact`, but the surface feels gappy.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Dive into the world of npm with our NPM Package Info MCP. Access crucial metadata about any npm
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