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mnemostack_answer

Synthesize a concise factual answer from retrieved memories, with confidence scores and source attribution.

Instructions

Answer a question using retrieved memories.

Read-only, no side effects, no authentication required. Use this when you want a concise factual answer synthesized from memory search results instead of the raw matches returned by mnemostack_search. Returns a JSON object with ok, query, answer text, confidence (0.0-1.0), sources, degraded (components that fell back while serving the call), fallback_recommended, tokens_estimate (estimated text tokens of the context memories), tokens_used (LLM-provider-reported usage for the answer call; null when unreported), and error. Stale facts are hidden by default; use include_invalidated or as_of to see them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNoPoint-in-time recall (ISO-8601); same contract as mnemostack_search.
limitNoMaximum number of results to return (default 10)
queryYesNatural language question or keyword to search memories for
filtersNoPayload filters applied inside every retriever (exact match or gte/lte ranges); the answer is generated only from memories inside the filtered scope.
token_budgetNoHard cap on the total (estimated) text tokens of the memories fed to the answer LLM (same contract as mnemostack_search). Unset uses the server-wide default.
include_invalidatedNoInclude facts marked stale (default false; same as mnemostack_search).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Discloses read-only, no side effects, no authentication required. Lists return fields including confidence, sources, degraded, fallback_recommended, and notes stale fact handling. No annotations provided, so description fully covers behavioral traits.

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?

Description is well structured and front-loaded with purpose and usage. Each sentence adds value; could be slightly tighter for extreme conciseness, but effectiveness is high.

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

Completeness4/5

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

Given 6 parameters and no annotations, the description covers return fields, default behavior for stale facts, and references sibling contracts. Output schema exists, so return value explanation is sufficient.

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 coverage is 100%, baseline 3. Description adds context beyond schema: explains as_of contract same as search, filters applied inside every retriever, token_budget same contract. Provides meaningful addition.

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 it answers a question using retrieved memories, distinguishing it from mnemostack_search which returns raw matches. The verb 'answer' and resource 'question' are specific.

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 want a concise factual answer synthesized from memory search results instead of the raw matches returned by mnemostack_search.' Provides clear context on when to use versus the sibling tool.

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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