Get details about online courses, games, articles and many more.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.2/5 across 6 of 6 tools scored.
Each tool has a distinct purpose: about provides an overview, list_* iterates over separate content types (articles, courses, games), read_article retrieves a full article, and subscribe handles email notifications. There is no overlap or ambiguity.
All tool names follow a uniform lower_snake_case pattern, with clear verb prefixes ('list_', 'read_', 'subscribe', 'about_'). The naming is intuitive and consistent across the set.
Six tools is a well-scoped number for a content-oriented server. Each tool covers a necessary action (overview, list each category, read an article, subscribe) without excess or redundancy.
The main workflows (browsing articles, courses, games; reading an article; subscribing) are covered. However, there is no way to retrieve details for a specific course or game beyond the list responses, and no search functionality, which could be minor gaps in a content discovery context.
about_geervaneeAInspectGeneral overview of Geervanee — what it is, what it teaches, and how to reach it. Use list_courses, list_articles, and list_games for the specific catalogues.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It implies a read-only informational tool by describing an 'overview,' but it does not explicitly state that no changes are made, whether authentication is needed, or what the response structure looks like. It covers content scope but not operational behavior.
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 two sentences with no filler. The first sentence front-loads the tool's purpose and content, and the second sentence efficiently points users to sibling tools for specific catalogues. 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—no parameters, no output schema, and no annotations—the description is sufficiently complete. It covers what the tool offers, what content it contains, how to reach Geervanee, and provides navigation to sibling catalogues, which fully satisfies the context needed to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is 100% covered, so there is no parameter documentation burden for the description. The description adds no parameter-specific information, but none is needed since the tool takes no inputs.
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 that the tool provides a general overview of Geervanee, including what it is, what it teaches, and how to reach it. It also distinguishes itself from sibling tools by explicitly mentioning that list_courses, list_articles, and list_games are for the specific catalogues.
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 second sentence gives explicit guidance on when to use sibling tools instead: 'Use list_courses, list_articles, and list_games for the specific catalogues.' This effectively frames this tool as the general overview while those handle specific content, providing clear when-to-use and when-not-to-use direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_articlesAInspectList Geervanee's published Treatise articles (title, category, summary, URL), optionally filtered by category. Use read_article with a slug from here to get the full text.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max articles to return. Default 20, max 100. | |
| category | No | Only articles in this exact category. Omit to list every category. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that the output is a list with specific fields and that only summaries are returned, not full text. However, it omits any mention of side effects, authentication, rate limits, or pagination, which for a read-only list may be acceptable but leaves some behavioral aspects 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?
Two concise sentences: the first covers purpose and output, the second covers usage with a next step. Every word serves a function, no redundancy. Highly front-loaded and efficient.
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 listing tool with only two optional parameters and no output schema, the description is complete. It covers what is returned, the filtering option, and the integration point with read_article. It does not need to explain return types since there is no output schema, and the tool is straightforward.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are already well documented. The description adds minor value by repeating the 'optional filter' concept and hinting at the use of slugs, but doesn't go beyond the schema's existing explanations. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb 'List' and the resource 'Geervanee's published Treatise articles', including the fields returned (title, category, summary, URL) and the optional filter. It clearly distinguishes from siblings like list_courses and list_games by specifying the article-specific content and ties to read_article.
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 clear context: the tool is for list-level browsing, and directs users to read_article with a slug from this list for full text. It doesn't explicitly state when not to use it, but the flow is unambiguous and differentiates from the other listing tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_coursesAInspectGeervanee's live course catalogue — title, level, live/self-paced, teacher, duration, price, and (for live courses) real upcoming batch schedule. Pulled live from the same feed courses.php shows visitors, not a static list.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | 'live' (interactive classes) or 'video' (self-paced). Omit for both. | |
| level | No | fundamentals, intermediate, or advanced. Omit for all levels. |
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 discloses that the data is live and not static, which is a key behavioral trait. It also mentions the inclusion of real upcoming batch schedules for live courses, adding context beyond the schema. However, it does not describe potential rate limits, authentication needs, or what happens if the feed is unavailable, but given the simplicity, this is adequate.
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 concise, two sentences, and front-loaded with the key information. It includes the essential details without any fluff, making it efficient for an agent to parse.
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 (2 optional parameters, no output schema), the description is complete enough. It explains the data source, the fields returned, and the optional filters. It could mention the return format, but since there is no output schema, the description does not need to explain return values. The description is sufficient for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the parameters are already well-documented. The description adds context by explaining the meaning of 'live' and 'video' types and the levels, but it does not go beyond the schema. Since the schema already covers the parameters, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists Geervanee's live course catalogue with specific fields (title, level, live/self-paced, teacher, duration, price, batch schedule) and distinguishes it from a static list by noting it is pulled live from the same feed as the website. This is a specific verb+resource with clear scope, and it differentiates from sibling tools like list_articles and list_games.
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 when to use this tool (to get course information) and mentions the optional filters (type and level) in the schema, but it does not explicitly state when not to use it or mention alternatives. However, the context is clear enough for an agent to decide, and the sibling tools are distinct in purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_gamesAInspectList Geervanee's published Fun with Sanskrit games (daily quiz, crossword, word search) — title and category, not the questions/answers themselves.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | prasnottari (quiz), padavinyasah (crossword), or padanusandhanam (word search). Omit for all. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that this is a read-only operation (listing) and specifies the output scope (title and category, not content). This adds important behavioral context beyond the schema, though it doesn't discuss pagination or error conditions. For a simple listing tool, this is good.
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?
One sentence, front-loaded with the action and resource, and clearly states what is excluded. No wasted words.
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?
Tool is simple, lone optional parameter, no output schema. Description covers purpose, output scope, and excludes content. Could arguably mention that it returns a list (implied by
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already has 100% coverage: category has a description with valid values and 'Omit for all.' The description adds no additional parameter semantics beyond the schema. Baseline of 3 applies when schema is comprehensive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'Geervanee's published Fun with Sanskrit games', and specifies the output includes title and category but not content. It distinguishes itself from siblings (list_articles, list_courses) by naming the specific resource type and 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?
The description implies usage for listing games, with an explicit mention of what it returns (title/category) and what it excludes (questions/answers). While it doesn't explicitly say when not to use it, the sibling tools clearly cover other resource types, so context is clear. No exclusions stated, but for a simple listing tool this is acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_articleAInspectRead one published Treatise article in full (plain text) by its slug, including its FAQ if the article has one.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The article's slug, from list_articles. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does well: it specifies the output format (plain text), scope (full article), and the FAQ behavior (includes FAQ if present). It slightly lacks details on errors (e.g., invalid slug, rate limits) but provides key behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence, front-loads the primary action ('Read one published Treatise article in full'), and adds the FAQ detail without any fluff. Every word 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 1-parameter read tool, the description covers the essential behavioral details (full text, plain text, FAQ inclusion). It doesn't address error cases or output size, but these are minor given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and the single parameter 'slug' is already documented as 'The article's slug, from list_articles.' The description adds the article reading context but does not add much beyond schema; baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Read') and resource ('Treatise article in full (plain text) by its slug'), and adds unique value by mentioning the FAQ inclusion, which distinguishes it from sibling tools like list_articles.
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 (fetch full content by slug) but does not explicitly state when to prefer this tool over siblings or mention alternatives. It is clear enough for basic use but lacks explicit when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
subscribeAInspectSubscribe an email address to be notified about new Geervanee Treatise articles. Use this when a person asks their agent to subscribe them, or someone on their behalf, to Geervanee's articles.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The email address to subscribe. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It states the action (subscribe) and the effect (be notified about new articles), but it does not disclose potential side effects like sending a confirmation email, requiring double opt-in, or any rate limits. It is adequate but not rich.
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 concise, two sentences, and front-loaded with the action. Every word earns its place, and it is easy to scan.
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, no output schema, no nested objects), the description is complete enough. It explains the purpose and when to use it. It could mention what happens after subscribing (e.g., confirmation), but that is not critical for a simple subscription 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?
The schema description coverage is 100% for the single parameter 'email', which is described as 'The email address to subscribe.' The description adds no additional meaning beyond the schema, but since the schema is complete, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: subscribing an email address to receive notifications about new Geervanee Treatise articles. It uses a specific verb ('subscribe') and resource ('email address', 'Geervanee Treatise articles'), and it distinguishes itself from sibling tools that list or read content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: when a person asks their agent to subscribe them, or someone on their behalf. It does not explicitly mention when not to use it or alternatives, but the context is clear enough given the sibling tools are all about reading/listings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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