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yantrikos

YantrikDB MCP

by yantrikos
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Python
Hybrid

recall

Read-onlyIdempotent

Search stored memories by semantic similarity to answer user queries about past decisions, preferences, or context. Refine results when initial confidence is low.

Instructions

Search memories by semantic similarity, or refine low-confidence results.

MODES:

  • Search (default): recall("project architecture decisions")

  • Refine: recall("PostgreSQL vs MySQL decision", refine_from="database choice", refine_exclude=["rid1"]) (Relevance feedback moved to memory(action="feedback") in v0.10 — recall is now purely read-only.)

WHEN TO USE: conversation start (summarize the user's first message); when the user references past decisions, people, preferences, or "last time"; when unsure about something the user assumes you know. Refine when first confidence < 0.5. After USING a recalled memory, reinforce it via memory(action="feedback", rid=..., feedback="relevant"). For "what is the CURRENT/latest X", prefer memory(action="chain_head") — similarity favors the most-similar revision, not the newest.

QUERY: one short natural-language sentence (5-10 words), NOT a keyword list — keyword stuffing degrades quality. One focused question per call; separate calls for separate topics.

TRUST SIGNALS: each hit's why_retrieved may carry staleness warnings ("aged", "rarely confirmed", "superseded by a newer record"). Treat flagged hits as weak evidence — prefer fresher results or chain_head, and note the flag if you act on one anyway.

Args: query: Short natural language sentence (5-10 words). NOT a keyword list. top_k: Max results (default 10). 3-5 for focused, 10-20 for broad. memory_type: Filter: "semantic", "episodic", "procedural". domain: Filter: "work", "preference", "architecture", "people", etc. source: Filter: "user", "inference", "document", "system". namespace: Filter by namespace. include_consolidated: Include merged memories. include_superseded: v0.10 — recall EXCLUDES superseded records by default (current-by-default). Set True only for history / archaeology over a revision chain. expand_entities: Use knowledge graph boosting (default True). refine_from: Original query text to refine from. query becomes the refinement. refine_exclude: Memory IDs to exclude when refining.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
domainNo
sourceNo
namespaceNo
memory_typeNo
refine_fromNo
refine_excludeNo
expand_entitiesNo
include_supersededNo
include_consolidatedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=true, destructiveHint=false, idempotentHint=true), the description details that 'recall is now purely read-only' after v0.10 changes. It also explains trust signals like staleness warnings, which adds significant behavioral context.

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 with MODES, WHEN TO USE, QUERY, TRUST SIGNALS, and Args sections. While slightly lengthy, every section adds unique value and is front-loaded with the core purpose.

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 11 parameters, no schema descriptions, and an output schema present, the description covers all aspects: modes, usage guidelines, parameter details, trust signals, and interactions with other tools (e.g., memory(action='feedback')). It is fully complete.

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?

Despite 0% schema coverage, the description thoroughly explains each parameter: query (short natural language, not keywords), top_k (with ranges), memory_type, domain, source, namespace, include_superseded (with default behavior context), etc. This fully compensates for the schema gap.

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 memories by semantic similarity, or refine low-confidence results.' It uses a specific verb-search-and resource (memories), and distinguishes from siblings like 'remember' (create) and 'forget' (delete). Two modes are explicitly described.

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?

Provides explicit when-to-use scenarios: conversation start, referencing past decisions, low confidence refinement. Also gives when-not-to-use: for current/latest X, prefer memory(action='chain_head'). Includes post-use reinforcement guidance.

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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