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kernl

kernl

by fastslack
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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kernel_brain_ask

Searches all your memories—notes, mail, chats, contacts and graph—by meaning and words, merging results into one ranked list. Use it before answering questions about a person, project or topic.

Instructions

What do I know about X? Searches every memory at once — indexed tasks, notes, contacts and mail (brain), past chat messages, what agents have learned, and the people, tasks, mail, events and products in the graph — by meaning and by words, merges the findings into one ranked list, and shows what each result is connected to. Use it before answering a question about a person, project or topic.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "kernel_brain_ask"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYesWho or what to ask about, in free text ("Ada", "the Q4 report", "deploy failures").
relatedNoNeighbours to show per result from the graph; 0 to skip.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations supplied, the description carries the burden and does well: it discloses the hybrid retrieval mode (meaning + words), that results from multiple stores are merged into a single ranked list, and that each result includes its graph connections. It does not state that the operation is read-only, nor anything about latency or result-volume behavior, so it is rich but not complete.

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 opening question 'What do I know about X?' front-loads the intent and is immediately actionable. The subsequent source enumeration is long but each item earns its place by defining search scope; only the graph source list is slightly padded.

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 federated search across many stores with an output schema present, the description covers sources, matching strategy, and result shaping, which is what an agent needs to decide and interpret. The remaining gap is behavioral safety context (read-only status, cost) that no annotation supplies.

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 67%, so the baseline applies. The description's phrase 'shows what each result is connected to' gives semantic grounding for the `related` parameter, but it says nothing about `limit` or how ranking interacts with the limit, and `limit` itself lacks a schema description.

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 names a specific verb (searches by meaning and by words) and enumerates the resources searched: indexed brain content (tasks, notes, contacts, mail), past chat messages, agent learnings, and graph entities. This is far more than a restatement of 'kernel_brain_ask' and lets an agent distinguish it from narrower siblings without opening any schema.

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?

It gives an explicit trigger: 'Use it before answering a question about a person, project or topic,' which is a clear when-to-use rule. It stops short of naming when NOT to use it or pointing at alternatives (e.g. kernel_graph_ask or kernel_brain_recall for narrower retrieval), leaving the boundary to be inferred.

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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