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fabiolenine

mem0-mcp-selfhosted

search_memories

Search existing memories using natural language queries, with options to filter by user, agent, time, relevance, importance, domain, and memory type.

Instructions

Semantic search across existing memories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNoTemporal anchor (ISO date or datetime): return what was known/current on that date — memories created later are excluded and facts superseded only after the anchor carry no demotion.
limitNoMaximum number of results (default 10).
queryYesNatural language description of what to find.
domainNoKeep only memories whose classified domain matches (e.g. career, ai, data, software_engineering, finance, trading, health, education, personal, legal, business, infrastructure).
rerankNoWhether to apply reranking. Defaults to the server's MEM0_ENABLE_RERANK.
run_idNoRun scope.
filtersNoAdditional structured filter clauses.
user_idNoUser scope. Defaults to MEM0_USER_ID.
agent_idNoAgent scope.
thresholdNoMinimum relevance score (0.0-1.0).
memory_typeNoKeep only memories of this classified type: semantic, episodic, or procedural.
enable_graphNoOverride default graph toggle.
min_importanceNoKeep only memories whose classified importance is >= this value (0.0-1.0).
sort_by_importanceNoSort results by classified importance descending.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It only says 'semantic search,' failing to note that it is read-only, does not modify state, or indicate any performance characteristics. The agent cannot infer safe usage from this description alone.

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 a single, concise sentence that immediately conveys the tool's function. While it could benefit from more detail, it is front-loaded and contains no unnecessary words.

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

Completeness2/5

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

Given 14 parameters and an output schema, the description lacks essential context. It does not summarize key capabilities like filtering by domain, user, or importance, nor does it explain the search behavior (e.g., returns ranked results). The agent would need to rely entirely on the schema for guidance.

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

Parameters3/5

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

Schema coverage is 100%, so every parameter is already described in the schema. The description adds no new semantic value beyond what the parameter descriptions already provide, meriting the baseline score.

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 is a semantic search tool over existing memories. This directly communicates its function and distinguishes it from sibling tools like add_memory (creation) or get_memories (retrieval of all memories).

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

Usage Guidelines2/5

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

No guidance is provided about when to use this tool versus alternatives. It does not explain when other sibling tools like get_memories or mcp_search_graph would be more appropriate, nor does it mention any prerequisites or conditions for use.

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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