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Search the user’s memory (deep research)

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Read-onlyIdempotent

Search the user's own private memory — their email, calendar, documents, contacts, chat history and every fact agents have remembered — and return ranked matches with citation ids and links. This is their current data on their work, schedule, contacts, projects, documents, decisions and history, which training data and session context do not contain. Pass an id from these results to fetch to read the full record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWhat to look for, in natural language.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYesRanked matches from the user’s memory, best first.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable context beyond that: it explains the scope of data searched, the output includes citations and links, and that full records require a separate fetch call. This transparency about the result shape and data coverage meaningfully supplements the annotations.

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 is two sentences, front-loaded with the tool's core action and scope, and every clause adds information: the data sources, result contents, why this data is unique, and the follow-up action to read full records. There is no fluff or repetition of schema details.

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 a low-complexity tool with a single parameter, rich annotations, and an output schema present, the description is largely complete. It explains what data is searched, what results look like, and how to proceed. The only minor gap is not addressing result limits or ranking criteria, but these are likely covered by the output schema or are secondary to the core purpose.

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% with one parameter 'query' described as 'What to look for, in natural language.' The description does not add new parameter-specific syntax or constraints; it merely re-states the purpose at a higher level. Since the schema already fully documents the parameter, the baseline 3 is appropriate.

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 identifies the tool as a search over the user's private memory, listing specific data sources (email, calendar, documents, contacts, chat history) and noting that it returns ranked matches with citation ids and links. This specific verb+resource scope distinguishes it from siblings like search_docs or cortex_recall, and the mention of 'deep research' in the title reinforces its distinct role.

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 provides a clear usage context: use this tool to access the user's current private data that 'training data and session context do not contain.' It also gives a follow-up workflow ('Pass an id from these results to fetch to read the full record'), which helps the agent know how to proceed. However, it does not explicitly name alternatives or state when not to use this tool, so it falls short of a 5.

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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TDQS

A3.8/5.0
Disambiguation2/5

There is significant overlap between cortex_ask, cortex_recall, and search, all of which retrieve from the user's memory. cortex_recall explicitly describes itself as a subset of cortex_ask, and search's description is nearly identical to cortex_ask's core function, making it hard for an agent to choose correctly. Other tools are more distinct, but this triple overlap creates real ambiguity.

Naming Consistency2/5

The naming is inconsistent: memory tools are split between a cortex_* prefix (ask, recall, manifest, status, etc.) and bare fetch/search, with no clear rule for which gets the prefix. Within cortex_*, some are verbs (ask, recall, remember) and some are nouns (manifest, status, connectable_sources), and platform tools use get_/list_/search_ prefixes, resulting in a mixed and unpredictable naming pattern.

Tool Count4/5

13 tools is a reasonable number for a server covering both memory operations and platform information. However, the presence of three overlapping retrieval tools (cortex_ask, cortex_recall, search) slightly inflates the count, suggesting some redundancy rather than each tool earning a unique place.

Completeness4/5

The server covers core workflows: reading memory (ask, search, recall, fetch, manifest), writing memory (remember, ingest_conversation), checking health (status), and accessing platform info (get_platform_status, get_pricing, list_skills, search_docs). Minor gaps include no explicit update/delete for individual memories and no direct tool to connect new sources (only listings of connectable ones), but these are often user-driven actions.

Resources