YantrikDB MCP

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think

Process memories in small batches to consolidate similar entries, detect contradictions, and mine patterns. Also supports full maintenance cycles and targeted index repairs for database hygiene.

Instructions

Run incremental cognitive maintenance — processes a small batch per call.

DESIGNED TO BE CALLED OFTEN: Each call processes ~5 memories (configurable). Running regularly (e.g. at end of conversation) gradually maintains the entire database without blocking. Safe to call frequently.

MODES:

Default: incremental think() — consolidation + conflict scan + (optional) pattern mining on a small batch.
maintenance_cycle=True: run the v0.9.0 autonomous-hygiene "sleep cycle" — think + burn-down-conflicts + prune-triggers + recalibrate-importance + backfill-entities + auto-relate (+ optional split_oversized + repair_artifacts).
last_cycle_only=True: just fetch the last persisted maintenance-cycle summary (read-only, no work performed).
maintenance_op="backfill_entities"|"rebuild_vec_index"|"rebuild_graph_index": run ONE targeted index-maintenance op and return. (Moved here from stats in v0.10 so stats could become read-only.)

Args: run_consolidation: Merge similar memories (default on). run_conflict_scan: Detect contradictions (default on). run_pattern_mining: Mine cross-domain patterns (default off, slow). consolidation_time_window_days: Only consolidate memories within this window (default 7 days). consolidation_limit: Batch size — max memories to process per call (default 5). Keep small for fast returns. maintenance_cycle: Run the full autonomous hygiene cycle instead. last_cycle_only: Just fetch the last cycle summary (read-only). dry_run: For maintenance_cycle — preview without persisting changes. burn_down_conflicts / prune_triggers_too / max_pending_triggers / recalibrate_importance / backfill_entities / auto_relate_in_cycle / max_auto_relate_edges / split_oversized / split_min_chars / repair_artifacts: Maintenance-cycle knobs.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`dry_run`	No
`maintenance_op`	No
`last_cycle_only`	No
`split_min_chars`	No
`split_oversized`	No
`repair_artifacts`	No
`backfill_entities`	No
`maintenance_cycle`	No
`run_conflict_scan`	No
`run_consolidation`	No
`prune_triggers_too`	No
`run_pattern_mining`	No
`burn_down_conflicts`	No
`consolidation_limit`	No
`auto_relate_in_cycle`	No
`max_pending_triggers`	No
`max_auto_relate_edges`	No
`recalibrate_importance`	No
`consolidation_time_window_days`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv0.19.2Aug 19, 2026
- addedInput schema / properties / dry_run / anyOf
```
Added value: +[
+  {
+    "type": "boolean"
+  },
+  {
+    "type": "null"
+  }
+]
```
- changedInput schema / properties / dry_run / default
```
Previous value: -trueNew value: +null
```
- removedInput schema / properties / dry_run / type
```
Removed value: -"boolean"
```

Changed1 schema field changedv0.10.0Jul 24, 2026

addedInput schema / properties / maintenance_op

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Maintenance Op"
+}

First observedv0.9.0Jul 8, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Given annotations only provide shallow hints (readOnlyHint=false, destructiveHint=false), the description carries the behavioral burden and does so thoroughly. It discloses side effects (consolidation, conflict scan, pruning, recalibration), the non-blocking incremental design, the slow pattern-mining option, dry-run behavior, and read-only last_cycle_only mode. This goes well beyond annotations and there is no 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?

Although lengthy, the description earns its length through structured sections (MODES, Args) and immediately front-loads the core purpose and operational guidance. There is no redundant filler; each sentence adds necessary information for correct invocation of a complex 19-parameter tool.

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?

The description is complete for the tool's complexity: it covers default behavior, alternative modes, per-parameter semantics, performance characteristics, read-only vs. mutating operations, and safe call frequency. Since an output schema exists, return-value details are not required. No meaningful gaps remain.

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 description coverage is 0%, so the description must compensate. It does: the 'Args' section explains all 19 parameters, including defaults and semantic intent. The MODES section additionally documents maintenance_op's accepted values and behavior. This turns an effectively opaque schema into a usable interface.

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 opens with a specific verb and resource: 'Run incremental cognitive maintenance — processes a small batch per call.' It clearly distinguishes the main behavior and further clarifies multiple modes (default, maintenance_cycle, last_cycle_only, maintenance_op), making the tool's purpose unambiguous even among sibling tools.

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

Usage Guidelines4/5

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

The description explicitly states when to use it: 'DESIGNED TO BE CALLED OFTEN' and 'Running regularly (e.g. at end of conversation) gradually maintains the entire database without blocking.' It also explains the different modes and when each is appropriate. It does not explicitly contrast with sibling tools, but the intended call cadence and mode selection provide strong usage 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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