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search_unified

Find relevant memories across short-term and long-term storage in one unified query, with ranking and filters for tags, status, and scores.

Instructions

Search across STM and LTM with unified ranking.

Args: query: Search text (max 50k chars). tags: Filter by tags (max 50). status: Filter STM by status ('active', 'promoted', 'archived' or list of these). Defaults to ['active', 'promoted'] if None. limit: Max results (1-100). stm_weight: STM multiplier (0.0-2.0). ltm_weight: LTM multiplier (0.0-2.0). window_days: Recent STM only (1-3650 days). min_score: Min STM score (0.0-1.0). page: Page number (default: 1). page_size: Results per page (10-100, default: 10). preview_length: Content chars (0-5000, default: 300).

Returns: Dict with results from both STM/LTM and pagination metadata.

Raises: ValueError: Invalid parameters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
tagsNo
limitNo
queryNo
statusNo
min_scoreNo
page_sizeNo
ltm_weightNo
stm_weightNo
window_daysNo
preview_lengthNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.1

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden and does a solid job: it discloses the return shape, pagination metadata, and ValueError on invalid parameters, and lists ranking/filter behavior. It does not explicitly state read-only/no-side-effect status, but the verb 'Search' and absence of mutation language make the behavior reasonably clear.

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?

The description opens with a one-line purpose, then uses compact Args/Returns/Raises sections that are information-dense without padding. Given 11 parameters, the length is justified and every line earns its place.

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?

For an 11-parameter, all-optional search tool with no annotations, the description covers parameters, return type, pagination metadata, and error behavior, which is largely complete. It leaves two minor ambiguities: how limit and page_size interact, and whether tags apply to both STM and LTM.

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%, and the description compensates fully: every parameter is explained with type, allowed range, default, and meaning, including query max length, status default, weight ranges, window_days range, and page_size constraints. This is exactly the semantic enrichment the schema lacks.

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?

The first line names a specific action (search), a clear resource scope (both STM and LTM), and the unified-ranking behavior. It is not a tautology and is understandable, but it does not explicitly contrast with search_memory or other siblings, so differentiation is left to inference.

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

Usage Guidelines3/5

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

The description implies its use case: run a combined STM/LTM search with configurable ranking and filters. There are no explicit when-to-use, when-not-to-use, or alternative-tool statements, and a sibling search_memory exists, so the agent must infer routing from the name and first line.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.