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dipseth

google-workspace-unlimited

by dipseth
OverviewSchemaRelated ServersScoreDiscussions
Python

Semantic Search

semantic_search

Search past tool responses and card templates stored in a vector database. Use natural language, filters, or analytics to retrieve usage history and results.

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv3.0.0
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "type": "object",
-  "x-fastmcp-wrap-result": true
-}New value: +null
  2. Addedv2.3.2

TDQS

A4.9/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full behavioral burden. It explicitly states the tool is read-only, that no Google APIs are called, the exact return row format, the two error messages ('Search failed', 'No results'), and the effect of score_threshold. This is far more transparent than typical tool descriptions.

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 well-structured and front-loaded: purpose, use-when, modes, behavior, returns, errors, then reference material. It is long, but the DSL symbol tables and examples earn their place because the schema refers to 'see docstring for symbols.' A minor redundancy between the 'Modes' section and the query parameter description prevents a perfect score.

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 a complex 11-parameter tool with no output schema and no annotations, this description is remarkably complete: it covers return shape, error behavior, default threshold semantics, DSL syntax, example queries, sibling routing, and parameter mode mappings. An agent has nearly everything needed to invoke it correctly and interpret the response.

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?

Schema coverage is 100%, giving a baseline of 3, but the description adds substantial meaning beyond the schema: a DSL symbol legend, a worked filter_dsl example, a MatchAny example, mapping of query_dsl/filter_dsl/prefetch_dsl to modes, explanation of positive/negative point IDs, and guidance to lower score_threshold. This meaningfully helps an agent construct parameters correctly.

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?

Description opens with a specific verb and resource: 'Search the Qdrant vector store of this server's past tool responses and card templates.' It also names sibling tools and clarifies what they do instead, so an agent can distinguish semantic_search from search and fetch_document without ambiguity.

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 'Use when' section explicitly lists trigger conditions: looking up previous results, usage history, or analytics, with concrete query examples. It also provides exclusion guidance: 'For discovering tools to call, use search; to preview one stored document, pass its point ID to fetch_document.' This is exactly the when/when-not context an agent needs.

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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