Connect to the MCP Studio SDK MCP server. This server is connected to two sources: the MCP Studio SDK documentation and the GitHub sample application repos. These resources are great for individuals looking to embed MCP Studio SDK into their web applications, and need an easy way to connect to an MCP server that has access reliable resources for AI-assisted engineering workflows.
Ask a natural language question and get an answer synthesized from all connected sources.
Returns passages, not a finished answer — you still have to read them and decide. Respect the version label on each passage: a "version-pinned" passage describes that release only, so do not present its values as current. If the passages conflict, lead with the current-docs one and note the conflict. Cite the Source URL for every fact.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Your question |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full burden and does so extremely well. It discloses that the returned result is incomplete ('passages, not a finished answer'), requires agent judgment, and honorsting versioning plus conflict-handling rules. It also mandates citing Source URLs, adding behavioral expectations beyond the input 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?
Everything is compact and front-loaded: purpose, then payoff behavior, then nuance. The description is dense but every sentence carries information — no fluff, no repetition of the input schema.
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?
Since there is no output schema, the description compensates by fully specifying the return shape ('passages'), semantics ('version -pinned', conflicts, citation). All important behaviors an agent needs to call it correctly and interpret results are stated.
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 only parameter, 'question', is already fully documented in the schema as 'Your question'. The description doesn't add extra parameter-level detail, but with 100% schema coverage, that's acceptable — this hits the baseline 3.
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 names a specific action ('Ask a natural language question') and a clear resource scope ('all connected sources'), distinguishing it from narrower or more targeted sibling tools like search_docs or query_source. It also clarifies the output type ('passages') rather than a finished answer, which prevents the agent from confusing it with a summarizer or direct-answer tool.
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 usage context is clear: it is a cross-source natural-language QA tool, and it gives explicit downstream instructions: read the passages, respect version labels, prefer the current-docs one on conflict, and cite the Source URL. It could name sibling tools or state exclusions explicitly, but it offers enough context for an agent to decide when to call it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract data schemas, type definitions, interfaces, and data models from documentation.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Schema or type to find |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It clarifies the scope (schemas, type definitions, interfaces, data models from documentation) but does not disclose return format, read-only nature, or limitations. This is a neutral score given the absence of contradictions.
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, concise sentence with no wasted words. It clearly communicates the tool's purpose and resource scope in a compact form.
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 simple one-parameter schema and lack of output schema, the description covers the core purpose, but it omits guidance on result contents, error behavior, or how to choose among sibling tools. This leaves some gaps for an agent navigating the documentation-tool set.
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 100% — the 'query' parameter is already described as 'Schema or type to find.' The tool description adds value by enumerating additional artifact types (interfaces, data models) but does not substantially extend beyond what the schema already provides.
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 ('Extract') and a clear resource ('data schemas, type definitions, interfaces, and data models') from 'documentation.' This distinguishes it from sibling tools like search_docs or query_source, which focus on broad search or source code.
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 by naming the artifact types and source, but it does not explicitly mention when to prefer this tool over alternatives such as search_docs or query_source, nor does it provide exclusions. The context is clear but not fully elaborated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract code snippets and examples from source documentation. Ideal for finding implementation patterns.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | What code to find | |
| language | No | Programming language filter |
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 does not state whether the tool is read-only, what the output format is, or any side effects. The term 'Extract' implies reading, but that is implicit rather than explicit, leaving significant 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 consists of two concise sentences. The first sentence states the main action and resource, and the second adds a use case. Every word earns its place, with no redundancy or unnecessary detail.
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 only two parameters and no output schema, the description provides the core purpose and a usage hint. However, it fails to explain the return format or output structure, which is a gap given the absence of an output schema. 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?
The input schema provides full descriptions for both 'query' and 'language' parameters, achieving 100% schema description coverage. The tool description adds no additional parameter details beyond what the schema already states, so the baseline score 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 function with a specific verb ('Extract') and resource ('code snippets and examples from source documentation'). It distinguishes from siblings like search_docs by focusing on code examples, but does not explicitly contrast with query_source or extract_schema, so it falls short of a 5.
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 phrase 'Ideal for finding implementation patterns' provides a clear use case for when this tool is appropriate. However, it does not mention alternatives or when not to use it, so it lacks explicit exclusions and alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract setup instructions, quickstart guides, and getting-started content from sources.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Specific topic | |
| source_url | No | Source URL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only states 'extract from sources' without explaining how extraction happens, what the output looks like, whether network calls are made, or any constraints. This leaves significant behavioral ambiguity.
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, concise sentence that front-loads the action and resource. No wasted words or redundant details.
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 two optional parameters and no output schema, the description leaves critical gaps: what does 'sources' mean, is source_url expected, and what happens if no parameters are provided? The tool lacks enough context to be fully understood in isolation.
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% with 'Specific topic' and 'Source URL', so the baseline is 3. The description adds context that the content is quickstart-related, but does not elaborate on parameter formats or required relationships between topic and source_url.
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 'Extract' and identifies the resource as 'setup instructions, quickstart guides, and getting-started content', clearly distinguishing this tool from sibling tools like get_code_examples or search_docs. The purpose is unambiguous and actionable.
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 when quickstart/setup content is needed, but it gives no explicit when-to-use or when-not-to-use guidance, and does not mention alternatives. This is adequate but lacks explicit decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query a specific source URL for targeted information. Fetches and extracts content matching your question.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | What to find | |
| source_url | Yes | URL to query |
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 the tool fetches a URL and extracts content matching the question, which implies a read-only operation. However, it does not mention limitations (e.g., URL type, content format, potential failures) or explicitly confirm no side effects, though these are not critical for a simple query tool.
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 short, purposeful sentences that immediately convey the tool's purpose and mechanism. No wasted words or redundant phrasing.
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 parameters, no output schema, no nested objects), the description adequately covers the operation. It explains the input purpose and the action taken. Minor gaps like response format or constraints are not critical for such a straightforward 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 coverage is 100%, meaning both parameters are already described in the schema. The description adds no new semantic detail beyond what the schema provides, so it meets the baseline but does not exceed it.
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 a specific verb ('Query') and resource ('a specific source URL'), with a scoped outcome ('for targeted information'). It distinguishes itself from sibling tools like search_docs or ask_question by focusing on a particular URL rather than a general search.
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 it—when you have a specific source URL and a targeted question—but does not explicitly mention alternatives or exclusion cases. There is no stated 'use this instead of X' guidance, so usage context is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full-text search across all connected documentation sources. Returns relevant passages and their source URLs.
Each passage is labelled with the documentation it came from. Prefer passages marked "current docs" over "version-pinned" ones for minimum requirements, supported versions, defaults, environment variable names, and command syntax. Answer only from the passages returned; if they do not cover the question, say so instead of relying on recollection. Quote variable names, commands, and version numbers exactly, and cite the Source URL for each fact.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| max_results | No | Max results (default 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure. It reveals that results are labeled by docs source, that current vs version-pinned judgments matter, and that responses must be grounded in returned passages with exact quoting and citation. That goes well beyond schema. It could add more on ordering, failure, or error behavior, but remains genuinely 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 front-loaded with the core definition, then each sentence adds a distinct useful behavior: result labelling, version preference, evidence-only answering, and exact quoting/citing with 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 two simple params, no output schema, and no annotations, the description covers enough: returns passages and URLs, labels them by source, gives selection heuristics, and instructs evidence-specific answering. This is complete for an agent to invoke and use 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?
Schema description coverage is 100%, so the baseline of 3 applies. The description does not add parameter-specific meaning beyond full-text search, but the schema already says query is a search query and max_results allows default.
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 clear verb-resource pair: full-text search across all connected documentation sources, and it explicitly says it returns passages plus source URLs. This scope distinguishes it from sibling tools like search_issues (issues) and get_code_examples (code examples), even without naming them.
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 by instructing the agent to prefer current over version-pinned docs for requirements, defaults, etc., which points to when the tool's results are authoritative. However, it never explicitly states when to choose this tool over siblings such as ask_question or query_source, and it doesn't give when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for known issues, bugs, workarounds, and solutions across connected sources.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Issue or error to search for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It adds the context that searches span 'connected sources', but does not disclose return format, pagination, or whether it is strictly read-only (though inferred from 'Search'). This 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 a single, front-loaded sentence that directly states the tool's function. Every word adds value with no redundancy or filler.
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 one-parameter search tool with no output schema, the description is mostly complete: it explains what it searches and why. It stops short of explicitly stating what is returned, but the purpose is clear enough for an agent to invoke 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?
Schema coverage is 100% for the single 'query' parameter, with a description that already clarifies it accepts an issue or error. The tool description adds context about the search scope (known issues, bugs, workarounds) but does not introduce new parameter semantics.
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 'Search' and clearly identifies the resource as 'known issues, bugs, workarounds, and solutions across connected sources'. This distinguishes it from sibling tools like search_docs and query_source by focusing on problem/solution 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: use this tool when you need to find known issues, bugs, or workarounds. It does not explicitly name alternative tools, but the domain is specific enough to imply appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a concise summary of a source page or section. Great for getting quick overviews.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | Specific aspect to focus on | |
| source_url | Yes | URL to summarize |
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 does not mention side effects, safety, errors, or prerequisites. While 'generate a summary' implies a non-mutating operation, it doesn't explicitly state read-only behavior or any limitations.
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 only two sentences with no filler, front-loading the key action and purpose. It earns its place efficiently without 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?
With no annotations and no output schema, the description should provide more context about return format, limitations, or operational behavior. It only offers a high-level purpose, leaving gaps for a tool that still has two parameters and potential edge cases.
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% with both 'source_url' and 'focus' already described in the schema. The description adds no extra parameter-level meaning, so the baseline of 3 applies per the rating guidelines.
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 action 'Generate a concise summary' and identifies the resource as 'a source page or section.' This is a specific verb+resource pairing that distinguishes it from sibling tools like search_docs or ask_question, which focus on different operations.
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 phrase 'Great for getting quick overviews' provides implied usage context, but the description lacks explicit guidance on when to use this tool versus alternatives or when not to use it. There are no exclusions or comparisons to sibling tools.
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
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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Add one secure layer between your agents and this server.
Several tools overlap in purpose: ask_question and search_docs both return relevant passages across connected sources, and search_issues partially duplicates that for known bugs. The more specialized tools are distinct, but an agent could easily pick search_docs instead of ask_question and still get similar results.
All 8 tool names follow a consistent lower_snake_case verb_noun pattern with descriptive verbs like ask, extract, get, query, search, and summarize. There is no mixing of naming conventions, abbreviated verbs, or ambiguity in the naming style.
8 tools is well within the ideal 3-15 range for a documentation-focused MCP server. Each tool covers a plausible distinct workflow: asking, searching, summarizing, extracting schemas, code examples, quickstarts, and known issues, so none feels unnecessary.
The tool surface covers the core read-oriented documentation workflow well: search, ask, summarize, targeted source queries, schema extraction, code examples, quickstarts, and issue lookup. Minor gaps exist such as no way to list connected sources or retrieve a full raw page, but agents can work around these using query_source and search_docs.