Kannada/English Carnatic flute KSGMS syllabus — search lessons, log practice, ask a RAG guru.
Ask VenuVidya — the in-app RAG chatbot. Returns a grounded answer + citations.
Args:
question: Student question (2-1500 chars).
level: Optional level filter for retrieval ('junior' | 'senior' | 'vidwat').
language: 'en', 'kn' or 'auto'.
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | ||
| language | No | auto | |
| question | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must compensate. It states the tool returns grounded answers with citations, but does not detail read-only nature, authentication requirements, or behavior under failure (e.g., no good answer). Some traits are implied but not explicit.
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 plus an Arg list. Front-loaded with purpose. Every sentence adds value, no waste. Ideal length for quick comprehension.
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?
With no output schema, the description should detail return structure (beyond 'grounded answer + citations'). Missing info on error responses, pagination if any, or behavior for empty results. Context signals show 3 params, 1 required, but description covers them adequately. Could be more complete.
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%, so description adds crucial constraints: question character limit (2-1500), level filter (junior/senior/vidwat), and language options (en, kn, auto). These go beyond the schema which only names parameters. Still missing expected value constraints for level (null or string) and language defaults.
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?
Description uses specific verb 'Ask' and resource 'VenuVidya' (the in-app RAG chatbot), clearly distinct from sibling tools that deal with classes, lessons, practice, and syllabus.
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?
Tool's purpose as a Q&A chatbot is clear, and sibling tools cover different domains, so when to use it is implicitly understood. However, no explicit when-not-to-use or alternative suggestions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a single class journal entry with its items and practice log. Only returns it if it belongs to your account (mcp_token).
Args:
class_id: The class to fetch.
mcp_token: Your personal VenuVidya token — GET /api/auth/mcp-token.
| Name | Required | Description | Default |
|---|---|---|---|
| class_id | Yes | ||
| mcp_token | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains that the tool fetches a specific entry with items and practice log, and importantly discloses the ownership check (only returns if it belongs to your account) and the auth token requirement (mcp_token). It does not mention error handling or response format, but as a read operation the verb 'fetch' implies non-mutating. The ownership constraint is a meaningful addition beyond the schema.
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 two-sentence purpose statement plus a two-line Args list. The main purpose and key constraint are front-loaded, and every sentence adds value. There is zero fluff.
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 fetch tool, the description covers the essential aspects: what it fetches (single class journal entry with items and practice log), the ownership constraint, and the argument meanings. It does not detail the exact response fields, but with no output schema, a little more on the returned structure could be helpful. However, the tool's simplicity and the described content make it adequate. No crucial information for correct invocation is missing.
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?
Despite 0% schema description coverage, the description fully explains both parameters in the Args section: class_id is 'The class to fetch' and mcp_token is 'Your personal VenuVidya token — GET /api/auth/mcp-token', including how to obtain it. This adds significant meaning beyond the schema's type-only definitions and completely compensates for the low schema coverage.
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 a specific verb ('fetch') and a specific resource ('a single class journal entry') with its associated items and practice log. This clearly distinguishes it from siblings like list_classes (which lists many) and get_lesson (which fetches 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving a single class journal entry but does not explicitly state when to use this tool versus alternatives like get_lesson or list_classes. It provides the ownership condition ('Only returns it if it belongs to your account') but no explicit when-not-to-use or alternative routing. This leaves some inference required.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the full study content of a single lesson.
Args:
lesson_id: Lesson identifier, e.g. 'junior-theory-1-4'.
| Name | Required | Description | Default |
|---|---|---|---|
| lesson_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It only states 'Fetch', implying a safe read operation, but omits details like output format, expected size, or any 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, no wasted words. Parameter documentation is integrated clearly. Efficient for a simple tool.
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?
Adequate for a simple fetch: explains what it does and the parameter. However, no output schema or mention of return content, which may leave agents guessing about the 'full study content' structure.
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%, so description must compensate. It explains 'lesson_id' with an example 'e.g. 'junior-theory-1-4'', adding useful context beyond the schema property name.
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 'Fetch' and the resource 'full study content of a single lesson'. It distinguishes from siblings like 'list_lessons' (which lists multiple) and 'get_class' (different resource).
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 purpose is clear—use to get full content of one lesson—but there is no explicit guidance on when to avoid it or comparison with alternatives (e.g., 'list_lessons' for overview).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List recent class journal entries (date descending) for YOUR account.
Args:
mcp_token: Your personal VenuVidya token — GET /api/auth/mcp-token
while signed in to the app. Scopes results to your journal only.
level: optional level filter.
limit: max number of classes (1-100, default 20).
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | ||
| limit | No | ||
| mcp_token | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It does disclose ordering (date descending) and scoping (YOUR account), and explains the token requirement. However, it does not mention potential errors (e.g., invalid token, rate limits) or whether the operation has any side effects beyond reading. This is adequate but not fully transparent.
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 and well-structured. It opens with a one-line summary, then breaks parameters into an 'Args:' list with each on its own line. No fluff, no repetition. Every sentence serves a purpose.
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?
In the absence of an output schema, the description does not explain what fields each class entry contains, nor does it mention error behavior or pagination beyond the limit parameter. For a simple list tool this is acceptable, but the gaps around return format and failure modes leave the agent somewhat under-informed.
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 the description must compensate. It does so effectively: mcp_token is explained with an acquisition method and scoping, limit is given a range and default, and level is identified as an optional filter. The only gap is that level does not enumerate possible values, but overall the parameter explanations add meaning well beyond the bare 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 opens with a specific verb-resource phrase: "List recent class journal entries (date descending) for YOUR account." It clearly identifies the action (list), the target (class journal entries), and a distinguishing scope (YOUR account). This is enough to set it apart from sibling tools like list_lessons or get_class without ambiguity.
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 the tool (when you need your own recent class entries) but does not explicitly state alternatives or exclusion criteria. There is no mention of 'use instead of list_lessons' or 'not for other users' accounts.' The guidance is implicit rather than explicit, so it earns a 3.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List lesson metadata. Filter by level/category.
Args:
level: 'junior' | 'senior' | 'vidwat'.
category: e.g. 'theory-1', 'practical-1', etc.
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | ||
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description must disclose behavioral traits. It states the operation (list, filter) but omits return format (e.g., what 'metadata' includes), pagination, rate limits, or side effects. This is insufficient for a listing tool with no output schema.
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 plus args list) and front-loaded with purpose. No wasted words; structured cleanly with args in a bullet-like format.
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 no annotations, no output schema, and sibling tools, the description lacks completeness. It does not specify default behavior (e.g., what happens with no filters), return structure, ordering, or pagination, which are critical for a listing 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?
Schema description coverage is 0%, but the description provides possible values for 'level' and an example for 'category', adding meaning beyond the schema. It compensates well for the lack of schema descriptions.
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?
Description uses specific verb 'List' and resource 'lesson metadata', and mentions filtering by level/category. This clearly distinguishes it from siblings like get_lesson (single lesson) and list_classes (different resource).
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/filtering lessons but does not explicitly state when to prefer this over siblings like get_lesson or search_syllabus. No direct comparison or exclusion criteria provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Log a practice session against a class item in YOUR journal.
Args:
class_id: Parent class id.
item_id: Item id inside the class (kriti/varna/etc.).
practiced_on: Date in YYYY-MM-DD.
mcp_token: Your personal VenuVidya token — GET /api/auth/mcp-token.
Only ever writes into a class this account owns.
duration_min: Duration in minutes.
notes: Free-text notes.
rating: Self-rating 1-5.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | ||
| rating | No | ||
| item_id | Yes | ||
| class_id | Yes | ||
| mcp_token | Yes | ||
| duration_min | No | ||
| practiced_on | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden. It mentions the required mcp_token and the ownership restriction, which are useful behavioral traits. However, it does not disclose return values, error handling, idempotency, or potential side effects beyond the write itself.
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: a one-sentence purpose followed by a bullet list of args with brief explanations. It front-loads the core action and avoids redundancy, making it 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 lack of an output schema, the description should clarify what the tool returns or possible errors. It covers parameter semantics and ownership constraints but omits return/error behavior, leaving some uncertainty for an agent.
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 description individually explains every parameter with meaning beyond the schema, including date format (YYYY-MM-DD), item type (kriti/varna/etc.), token acquisition endpoint, and the ownership constraint. This fully compensates for the 0% schema coverage.
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 a specific verb ('Log') and resource ('a practice session against a class item') with scope ('in YOUR journal'), clearly distinguishing from read/query siblings like get_class and list_classes. It also implies a write operation, which aligns with the tool's function.
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 does not provide explicit when-to-use guidance, exclusions, or alternatives. It only states the purpose and a constraint (writes only into owned classes), leaving the agent to infer when to invoke this tool versus siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Keyword (BM25) search across all Carnatic flute lessons.
Args:
query: Natural-language question or keywords (English or Kannada).
k: Number of top results to return (1-10, default 4).
level: Optional filter — 'junior', 'senior' or 'vidwat'.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | ||
| level | No | ||
| query | 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 discloses the search algorithm (BM25) and parameters. It does not explicitly state side effects or read-only nature, but for a search tool this is reasonably transparent. The mention of BM25 adds behavioral detail.
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 clear sentence followed by a structured parameter list. Every sentence adds value, and the key information is front-loaded. 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?
Given the low complexity, no output schema, and no annotations, the description covers the core functionality and parameter usage. It lacks details about output format or ordering, but for a simple search tool with k results it is adequately complete.
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%, and the description compensates fully by explaining each parameter: query as natural-language query, k as number of results (1-10, default 4), and level as optional filter with specific values (junior, senior, vidwat). This adds significant meaning 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's a 'Keyword (BM25) search across all Carnatic flute lessons', which is a specific verb and resource. It distinguishes from sibling tools like get_lesson (individual retrieval) and ask_venuvidya (likely Q&A).
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 searching across lessons but does not explicitly state when to use versus alternatives (e.g., ask_venuvidya for questions, list_lessons for overview). No when-not or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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
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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
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The server is experiencing an outage
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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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Vedic astrology (Jyotish): birth charts, divisional charts, dashas, yogas, transits, panchang
Free Vedic astrology: panchang, kundali, transits, matching, doshas, eclipses, numerology
Vedic and Western astrology, numerology, tarot. Kundali, dasha, panchanga, Rajju/Vedha matching.
Jyotish, Panchang, Hindu festival and Sanatan Dharma knowledge tools with citations.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: ask_venuvidya is a RAG chatbot, search_syllabus is keyword search, list/get_lesson and list/get_class handle distinct resources, and log_practice_session writes practice data. No two tools have overlapping responsibilities.
All tools follow a consistent verb_noun pattern in snake_case (ask, get, list, log, search). Even 'ask_venuvidya' uses a verb + proper noun, matching the style. Naming is predictable and scannable.
Seven tools cover the core flows of a study guide: browsing lessons, searching content, logging practice, and retrieving class journals. This is well-scoped—not too sparse, not bloated.
The surface covers viewing lessons and classes, searching, and logging practice, but lacks write operations for lessons or classes (e.g., annotating or updating). However, the server's role is a study guide, and the missing operations are not essential for its stated purpose.
