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mnemostack_search

Search indexed memories using hybrid recall with BM25, semantic, graph, and temporal retrievers. Returns ranked results with id, text, score, sources, and payload.

Instructions

Search indexed memories with hybrid recall.

Read-only, no side effects, no authentication required. Use this when you need raw memory matches rather than a synthesized answer. Returns a JSON object with ok, query, count, results, and degraded (which components fell back while serving the call; empty when healthy). Results are ranked by reciprocal rank fusion of BM25, semantic, graph, and temporal retrievers when configured; each result includes id, text, score, sources, and payload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language question or keyword to search memories for
limitNoMaximum number of results to return (default 10)
include_traceNoInclude the per-retriever recall trace (debug; verbose)
filtersNoPayload filters applied inside every retriever: exact match ({"tenant": "a"}) or gte/lte range ({"timestamp": {"gte": "2026-01-01"}}). Results never include points outside the filtered scope.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully carries the burden, disclosing read-only nature, no side effects, no authentication, return structure (fields like degraded and ranking method), without contradiction.

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

Conciseness5/5

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

Two sentences, no redundancy, front-loaded with purpose. Every sentence adds essential information without wasted words.

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?

Given the tool's complexity (hybrid search, multiple retrievers), the description covers purpose, usage, behavioral traits, return structure, and ranking without needing to explain an existing output schema. Adequately complete.

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

Parameters4/5

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

Schema description coverage is 100%, baseline 3. The description adds extra value beyond schema by explaining the filters parameter behavior ('Results never include points outside the filtered scope') and implicitly clarifying include_trace via the degraded field, justifying a 4.

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 clearly states 'Search indexed memories with hybrid recall' and distinguishes from siblings by specifying 'raw memory matches rather than a synthesized answer', which contrasts with the likely purpose of mnemostack_answer.

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?

Explicitly says 'Use this when you need raw memory matches rather than a synthesized answer', providing a clear when-to-use directive. Also notes it is read-only with no side effects or authentication required, guiding appropriate invocation without alternatives listed.

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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