cortex_recallSemantic-only vector search over the memory, returning source excerpts. Prefer cortex_ask, which also uses full-text and graph expansion; reach for this when you specifically want nearest-neighbour matches on meaning.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10). | |
| query | Yes | Search text. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that the search is 'semantic-only' and returns 'source excerpts', which are useful behavioral details consistent with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with purpose and usage guidance, with no wasted words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with only 2 parameters and no output schema, the description fully covers purpose, usage, and return type (source excerpts). It is complete and informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters have adequate descriptions in the schema (e.g., 'Search text', 'Max results default 10'). The description does not add further meaning beyond what the schema provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a 'semantic-only vector search over the memory, returning source excerpts'. It distinguishes itself from the sibling tool cortex_ask, which uses full-text and graph expansion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to prefer cortex_ask for broader search and to use cortex_recall specifically for nearest-neighbor matches on meaning, providing clear guidance on when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.