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cortex_memory_search

Search a user's Cortex memory by natural-language query to return ranked, cited bricks for grounded AI recall.

Instructions

Hybrid recall over a user's Cortex memory. Returns ranked, cited bricks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topKNoMax bricks (default 5)
queryYesNatural-language recall query
apiKeyNoYour Cortex tenant key. Required on shared/hosted servers; else the server key is used.
userIdYesEnd user id to recall for
minScoreNoMin score (default 0.25)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the retrieval mode ('hybrid') and the shape of results ('ranked, cited'), which is real behavioral context given there is no output schema. It omits permission/tenant-key implications, result limits, and behavior on empty matches.

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?

Two short sentences, front-loaded with purpose and followed by return characteristics; nothing is padded. It is arguably too terse for a five-parameter retrieval tool, but as a structure judgment it is efficient.

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?

A five-parameter tool with no annotations and no output schema needs more: no guidance on when to search versus store, no note on the apiKey tenant behavior, and no indication of how empty or low-score results behave. The description covers the surface but leaves the calling decision underspecified.

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 description coverage is 100%, so all five parameters including defaults for topK and minScore are already documented in the schema. The description adds no format, syntax, or semantic detail beyond it, so the baseline 3 applies.

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?

States a specific verb ('recall') and resource ('a user's Cortex memory'), and 'hybrid' signals the retrieval strategy (lexical + semantic). It does not differentiate itself from the sibling cortex_memory_store beyond the implicit read/write split, and 'bricks' is unexplained jargon.

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?

There is no when-to-use guidance and no alternatives named, even though cortex_memory_store is an obvious counterpart for writing memories. The agent must infer the routing from the name alone.

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