Read the full search results returned for a single instant-search request. Returns chunk_id, vertex_id, title, full text, and relevance score. Use this to inspect the exact results a user saw for their search query. Ordered by score descending.
| Name | Required | Description | Default |
|---|---|---|---|
| search_request_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds useful behavioral context: it returns full text, includes relevance scores, and is ordered by score descending. It does not mention rate limits, auth, or payload size, but for a simple read operation with strong annotations this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short, information-dense sentences: purpose, returned fields, usage scenario, and ordering. No redundant wording or filler; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a single parameter and an output schema, the description covers the purpose, the key returned fields, ordering, and the use case. It doesn't explicitly mention alternatives or prerequisites, but given the tool's simplicity and the available annotations, it is sufficiently complete for correct invocation.
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 for the sole parameter search_request_id. The description refers to 'a single instant-search request,' which contextualizes the ID as identifying that request. However, it doesn't explicitly describe the parameter's origin or format beyond what the parameter name and schema type (UUID) already convey.
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 ('Read') and resource ('full search results returned for a single instant-search request'), and lists the returned fields (chunk_id, vertex_id, title, full text, relevance score). This clearly distinguishes it from sibling tools like read_chat_request_documents by scoping to instant-search requests.
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 gives a clear use case: 'inspect the exact results a user saw for their search query.' It does not explicitly name alternatives or provide when-not-to-use conditions, but the context is clear enough for an agent to select this tool over related read tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or metric. The many get_top_* endpoints are differentiated by the specific dimension measured, and read_* / list_* / get_* verbs consistently separate detail retrieval from aggregation and paginated listings. Explicit distinctions like get_top_languages vs get_top_locales and get_top_interaction_sources vs get_top_clicked_urls remove ambiguity.
Tool names follow a predictable verb_noun pattern: create_* for mutations that add, update_* for edits, list_* for paginated collections, read_* for detailed record access, and get_* for aggregate analytics. Even with 33 tools the naming convention is uniform and readable.
33 tools exceeds the 25+ threshold for 'too many' and is heavy for a single server surface. While the analytics getters are individually focused, the set is larger than typical for an MCP server and could be consolidated or grouped more tightly.
Analytics coverage is thorough, and nodes/prompts have create/read/update lifecycles. However, there are no delete operations anywhere, and data sources and tools support update but not create or delete, leaving notable lifecycle gaps for administrative tasks.