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quipu_unified_search

Read-only

Search Bobbin-integrated knowledge by natural language, combining text and optional vector results with normalized scores; auto-embeds queries when configured and expands linked facts.

Instructions

Unified knowledge search for Bobbin integration. Combines text and optional vector search, returning results tagged with source='knowledge' and normalized 0-1 scores. When an EmbeddingProvider is attached, the query is auto-embedded for semantic search.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum results (default: 10)
queryYesNatural language search query
embeddingNoOptional pre-computed query embedding. When omitted and EmbeddingProvider is attached, query is auto-embedded.
expand_linksNoExpand results via graph links (default: true)
max_facts_per_entityNoMaximum facts per entity (default: 10)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.17.0

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so safety and scope are covered. The description still adds real value beyond that: results are tagged with source='knowledge', scores are normalized to 0-1, and the auto-embedding behavior is conditional on an EmbeddingProvider being attached. It omits result shape and pagination, but this is useful disclosure for the bar set by the annotations.

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?

Three sentences, front-loaded with the core function and with no filler. The phrasing 'for Bobbin integration' is a slightly opaque piece of context that does little work for an agent, but overall the structure is efficient.

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

Completeness3/5

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

With no output schema, the description carries the burden of explaining returns, and it only partly does so: it names the source tag and score normalization but not the result object structure or how expand_links affects output. Given five parameters and a dense sibling set, it is adequate but leaves meaningful gaps.

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

Parameters3/5

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

Schema description coverage is 100%, so every one of the five parameters is already documented in the schema. The description's note about auto-embedding largely restates the 'embedding' parameter description, and it adds nothing about limit, expand_links, or max_facts_per_entity. Baseline 3 applies since the schema does the heavy lifting.

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

Purpose4/5

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

States a specific verb and resource ('Unified knowledge search') and specifies the mechanism: combined text plus optional vector search. It does not, however, distinguish itself from close siblings such as quipu_search, quipu_hybrid_search, or quipu_search_query, so an agent cannot tell which of these to pick from the description alone.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no exclusions, and no named alternative. The crowded sibling space (quipu_search, quipu_hybrid_search, quipu_search_nodes, quipu_search_facts) makes this omission costly; 'for Bobbin integration' is contextual framing, not a selection rule.

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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