Sasha, Neural Partners Chief of Staff

Ask Sasha about Neural Partners

ask_sasha

Sasha, Neural Partners' Chief of Staff, answers questions about what Neural Partners sells: the Neural Core products and platform, the Neural Labs agency and advisory services, whether they fit a business, and how to get a price. structuredContent.links carries the Neural Partners pages her reply points to. One message per call; a conversation continues by conversation_id. Sasha, Neural Partners' AI agent on Claude, can be wrong. Prices are starting points only. Chats are kept up to 90 days. She never books or submits anything.

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": {
      "message": "<message>"
    },
    "name": "ask_sasha"
  }
}

Input Schema

Name	Required	Description	Default
`message`	Yes	What to ask or tell Sasha, in plain text.
`conversation_id`	No	The conversation_id an earlier call returned. Omitted, a new conversation starts. Earlier turns are kept server-side, so only the new message is sent.

Output Schema

Name	Required	Description
`error`	No	Present only on a result with isError: true.
`links`	No	The Neural Partners pages Sasha's reply pointed at, as absolute URLs. Only pages on the Neural Partners site are ever listed. Empty when she pointed at none.
`reply`	No	Sasha's reply, on its own. The disclosure is never part of it.
`degraded`	No	True when the reply is a stand-in because Sasha ran out of time; the same message can be sent again.
`disclosure`	No	Present on the first reply of a conversation: what the person should know about Sasha. Also sent as its own first content block.
`offer_handoff`	No	True on the reply where Sasha offers to pass the conversation to the Neural Partners team.
`conversation_id`	Yes	Continues this conversation on a later call.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 22, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the annotations, it discloses reliability caveats ('can be wrong', 'prices are starting points only'), data retention ('chats are kept up to 90 days'), a no-side-effect guarantee ('never books or submits anything'), and where the useful output lives (structuredContent.links). This also explains why readOnlyHint is false despite the read-like nature — conversations persist server-side — so there is no annotation conflict.

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?

It is front-loaded with the core scope, then layered with operational notes (links, conversation continuity) and caveats, so nothing important is buried. It is slightly dense and repeats 'Neural Partners' several times, but every sentence carries distinct information.

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

Completeness5/5

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

An output schema exists, yet the description still points to structuredContent.links, which is the piece an agent most needs for follow-up. Combined with the multi-turn mechanics, retention window, reliability caveats, and the no-booking guarantee, nothing essential for correct invocation is missing.

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

Parameters4/5

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

Schema coverage is 100% and the schema already documents both message and conversation_id, so the baseline is 3. The description adds genuine meaning on top: 'One message per call' clarifies the granularity of message, and 'a conversation continues by conversation_id' frames the parameter as multi-turn state rather than a plain identifier.

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 the actor (Sasha, Chief of Staff), the verb (answers questions), and the exact scope of topics she covers: Neural Core products, Neural Labs services, fit, and pricing paths. The closing line 'She never books or submits anything' implicitly fences this off from book_discovery, so an agent can route between the three siblings without opening a 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 clearly establishes the usage context (questions about what Neural Partners sells, fit, and price) and states a negative boundary ('never books or submits anything'), which signals that booking belongs elsewhere. It stops short of naming book_discovery or pricing_discovery as the alternatives, and 'how to get a price' overlaps with pricing_discovery without resolving which to prefer.

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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