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dipseth

google-workspace-unlimited

semantic_search

Search past tool responses and card templates by semantic similarity, history, or analytics. Retrieve previous results and usage stats from the vector store.

Instructions

Search the Qdrant vector store of this server's past tool responses and card templates.

Use when: looking up previous results, usage history, or analytics ('service:gmail last week', 'tool:search recent', 'overview'). For discovering tools to call, use search; to preview one stored document, pass its point ID to fetch_document.

Modes: semantic text similarity; service/tool history queries; analytics ('overview', 'usage stats'); filter_dsl for structured filters; positive/negative point IDs for recommendation; query_dsl for fusion/discover/order-by; prefetch_dsl for multi-stage retrieval.

Behavior: read-only against the local Qdrant instance; no Google APIs are called. Returns: scored rows 'score service/tool timestamp id:'. Errors: 'Search failed' when Qdrant is unreachable; 'No results' when nothing clears score_threshold (default 0.3 — lower it to widen the net).

DSL filter symbols: D_5=DatetimeRange, ʄ=FieldCondition, ƒ=Filter, F_6=FilterSelector, ℏ=HasIdCondition, I_2=IsEmptyCondition, I_0=IsNullCondition, ɱ=MatchAny, ṁ=MatchText, M_0=MatchTextAny, ☆=MatchValue, ř=Range Query-DSL symbols: Å=AcornSearchParams, C_14=ContextExamplePair, ¢=ContextPair, C_0=ContextQuery, D_6=DiscoverInput, D_2=DiscoverQuery, D_4=DiscoverRequest, D_21=DiscoverRequestBatch, ℱ=Fusion, φ=FusionQuery, ø=OrderBy, ɵ=OrderByQuery, ¶=Prefetch, ʔ=QuantizationSearchParams, R_12=RecommendGroupsRequest, R_2=RecommendInput, R_4=RecommendQuery, R_5=RecommendRequest, R_18=RecommendRequestBatch, R_10=RecommendStrategy, ♦=SearchParams Example: ƒ{must=[ʄ{key="tool_name", match=☆{value="send_dynamic_card"}}]} MatchAny: ƒ{must=[ʄ{key="tool_name", match=ɱ{any=["tool_a", "tool_b"]}}]}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results to return
queryYesSearch query — natural language, 'service:gmail recent', 'overview', 'id:<point_id>', or semantic text when filter_dsl is set
dry_runNoIf true with filter_dsl, parse and validate DSL without executing the query
query_dslNoQuery DSL for recommend, discover, fusion, or order-by queries
collectionNoQdrant collection to search. Default: mcp_tool_responses
filter_dslNoQdrant DSL filter notation for precise filtering (see docstring for symbols)
prefetch_dslNoMulti-stage prefetch DSL for hierarchical search strategies
score_thresholdNoMinimum similarity score (0.0-1.0)
user_google_emailNoUser's Google email (auto-injected by middleware)
negative_point_idsNoPoint IDs to use as negative examples for recommendation search
positive_point_idsNoPoint IDs to use as positive examples for recommendation search

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It explicitly states 'read-only against the local Qdrant instance; no Google APIs are called', describes return format, and enumerates error messages and the default score_threshold with advice to lower it. This is thorough and honest.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear sections (Use when, Modes, Behavior, Returns, Errors, DSL symbols). The front-loaded purpose sentence earns its place, and the extensive DSL reference is justified given the tool's complexity, though it edges toward verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 11-parameter tool with multiple DSL modes, the description is remarkably complete. It covers all modes, error cases, default behavior, and return format, while the output schema exists so return values need not be elaborated. The only minor gap is lack of explicit multi-tenant data isolation mention, but that is not required.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While schema description coverage is 100%, the description adds substantial meaning: it explains the semantics of filter_dsl, query_dsl, prefetch_dsl, positive/negative_point_ids, and provides a full symbol legend plus syntax examples. This goes well beyond the schema's generic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Search the Qdrant vector store of this server's past tool responses and card templates.' It clearly distinguishes itself from siblings by explicitly directing users to 'use search' for tool discovery and 'fetch_document' for previewing stored documents.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit 'Use when' guidance with concrete examples like 'service:gmail last week' and 'tool:search recent', and names alternatives for other scenarios. This is a model of when-to-use versus alternatives.

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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