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.
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.7/5 across 8 of 8 tools scored.
Most tools have clearly distinct purposes (e.g., extract_schema vs get_code_examples). Some overlap exists between ask_question and search_docs, and between query_source and summarize_content, but descriptions help differentiate them.
All tool names follow a consistent verb_noun pattern using snake_case (ask_question, search_docs, summarize_content). No mixed conventions or vague verbs.
With 8 tools, the server is well-scoped for a documentation/SDK querying purpose. Each tool serves a distinct function without unnecessary bloat.
The set covers core workflows: searching, asking questions, extracting schemas/code/quickstarts, summarizing, and searching issues. A minor gap is the lack of a tool to list available sources, but the current surface is largely complete.
ask_questionAInspectAsk a natural language question and get an answer synthesized from all connected sources.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Your question |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It indicates the tool synthesizes answers from all connected sources, suggesting a read-only, aggregation behavior, but it does not disclose limitations, potential latency, or how sources are selected. This leaves the agent without information on side effects or failure modes.
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?
A single sentence with no redundant wording. It front-loads the action ('Ask a natural language question') and specifies the resource ('all connected sources').
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 broad scope (synthesizing from all sources), the description lacks specifics about the output format, whether citations are provided, or what happens if no sources are connected. While the simple interface helps, the lack of an output schema or additional context leaves gaps for an agent to fully understand the tool's behavior.
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 covers the only parameter 'question' with 'Your question.' The description adds the qualifier 'natural language,' which advises the agent on the input format beyond the schema. This is a meaningful addition, though it does not specify length or example formats.
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: 'Ask a natural language question and get an answer synthesized from all connected sources.' It distinguishes from siblings like query_source (which likely queries a specific source) and search_docs (which searches documents) by emphasizing 'all connected sources' and 'synthesized.'
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 a use case: when you need a synthesized answer across multiple sources, rather than a direct lookup. However, it does not explicitly mention alternatives or exclusions, so while context is clear, it lacks explicit guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_schemaAInspectExtract 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.
get_code_examplesAInspectExtract 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.
get_quickstartBInspectExtract 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_sourceAInspectQuery 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.
search_docsAInspectFull-text search across all connected documentation sources. Returns relevant passages and their source URLs.
| 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 provided, the description carries the full burden of disclosing behavior. It states the tool performs a full-text search and returns passages with URLs, which implies a read-only, non-destructive operation. It does not mention any side effects, authentication prerequisites, or rate limits, but for a search tool the risk is low and the behavior is reasonably 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 two sentences long, front-loaded with the core action ('Full-text search'), and includes the key output (passages and URLs). Every sentence earns its place with no redundancy or irrelevant details. It is appropriately concise and well-structured.
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 2 parameters, no output schema, and a clear scope, the description is sufficiently complete. It covers what the tool searches, what it returns, and the source of results. While it could mention the default max_results value or sorting behavior, those are already documented in the schema, so the description provides enough context for effective use.
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 covers both parameters ('query' and 'max_results') with descriptions, achieving 100% schema_description_coverage. The tool description itself adds no additional parameter-level detail, so it falls at the baseline score of 3. The description's mention of 'relevant passages' aligns with the 'query' parameter but adds no new semantic meaning.
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 performs 'full-text search across all connected documentation sources' and specifies the output ('relevant passages and their source URLs'). This distinguishes it from sibling tools like search_issues by focusing on documentation sources. The verb 'search' and resource are explicit and specific.
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 that this searches across all connected documentation sources, which implies a broad use case. However, it does not explicitly mention when to prefer this tool over alternatives like query_source, nor does it state any exclusions (e.g., 'not for issues'). There is no comparison with siblings, leaving usage guidance implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_issuesAInspectSearch 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.
summarize_contentBInspectGenerate 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.
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