Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients
Retrieve complete cleaned content for a specific Apple developer documentation or video by URL. Returns the full processed content from Apple's official knowledge base.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the Apple developer documentation or video to retrieve content for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses that the output is 'cleaned' and 'processed' content, which goes beyond a generic 'fetch' description. It does not mention error handling or rate limits, but for a simple retrieval tool, the provided detail is solid.
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, both concise and front-loaded with the main action. There is minor redundancy between 'complete cleaned content' and 'full processed content', but overall it is appropriately sized 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 tool with a single parameter and no output schema, the description explains the input (URL) and the output (processed content). It is complete enough for a simple retrieval operation, though it could mention edge cases or authentication requirements to be fully exhaustive.
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 fully describes the single 'url' parameter with a clear description. The tool description adds no new semantic detail beyond what the schema already provides, so the baseline score of 3 applies.
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 verb 'Retrieve' is specific, and the resource is clearly 'complete cleaned content for a specific Apple developer documentation or video by URL'. This distinguishes fetch from the sibling search tool, which is implied to be for discovery rather than direct retrieval.
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 makes clear that this tool is for retrieving content via a specific URL, which implies use when you already have the exact location. It does not explicitly mention alternatives or exclusions, but the context is clear enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Apple's official developer documentation and video content using advanced RAG technology. Returns relevant content from Apple's technical documentation, frameworks, APIs, design guidelines, and educational resources.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query for Apple's official developer documentation and video content. Queries must be written in English and focus on technical concepts, APIs, frameworks, features, and version numbers rather than temporal information. | |
| result_count | No | Number of results to return (1-10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It notes the tool 'Returns relevant content' from specific sources, but lacks details on side effects (e.g., read-only nature), rate limits, or result format. The mention of 'advanced RAG technology' adds little behavioral transparency.
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 front-loaded with the core purpose. The phrase 'using advanced RAG technology' is slightly non-essential, but overall every sentence serves to explain scope and output.
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 search tool with full parameter coverage, the description adequately states the search scope and return content type. However, it does not clarify the relationship with 'fetch' or describe the result structure, which is acceptable given no output schema.
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 descriptions fully cover both parameters (100% coverage), so the description adds no extra parameter-level meaning. The baseline of 3 applies because the schema already documents query and result_count effectively.
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 'Search Apple's official developer documentation and video content' with a specific verb and resource. This differentiates it from the sibling tool 'fetch', which likely retrieves specific 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?
No explicit guidance on when to use this tool versus 'fetch' or any other alternative. The description only states the purpose, with no hints about preferred contexts or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
search1 field changedInput schema / properties / result_count / typePrevious value: -"number"New value: +"integer"fetch1 field changedInput schema / $schemaAdded value: +"https://json-schema.org/draft/2020-12/schema"search1 field changedInput schema / $schemaAdded value: +"https://json-schema.org/draft/2020-12/schema"fetchsearchClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Exa MCP — neural/semantic web search + content retrieval (exa.ai)
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Versioned documentation registry and semantic search for AI tools and coding assistants.
Add one secure layer between your agents and this server.
fetch and search have clearly distinct purposes: retrieving a specific document by URL versus searching across the content corpus. No overlap or ambiguity exists.
Both tool names are single verbs ('fetch', 'search') following a consistent action-oriented pattern. Naming is simple and predictable.
With only two tools, the set feels thin for a documentation server, though each tool is essential and well-scoped. The count is borderline per the calibration guidelines.
For a read-only documentation server, fetch and search cover the core workflows: accessing specific content and discovering relevant material. No significant gaps are apparent.