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Synaplan

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Synaplan chat

synaplan_chat

Send a message through Synaplan's full AI pipeline — intent classification, web search, RAG over the user's documents, long-term memories, and inference — and return the synthesized answer. This is richer than a raw model call: it uses the authenticated account's knowledge base and memories.

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": "synaplan_chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
agentIdNoOptional assistant id or slug (see list_assistants). Pins the reply to that assistant; fails when it is not published, not usable by you, or does not allow connected apps.
chat_idNoOptional existing chat id (from list_chats) to continue a conversation. Omit to start a new chat.
messageYesThe message or question to send through Synaplan's full AI pipeline (classification, web search, RAG over the user's documents, memories, and inference).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already flag readOnlyHint=false, openWorldHint=true, destructiveHint=false; the description adds genuine context beyond them by naming the pipeline stages (classification, web search, RAG, memories, inference) and the data sources the call touches. It omits that a chat is persisted and that web searches may incur cost/latency, which are meaningful side-effect details for a non-read-only tool.

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?

Two sentences, zero padding: the first front-loads what the tool does and what it returns, the second supplies the differentiating rationale. Nothing redundant with the schema.

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 one-required-parameter tool with no output schema, the description covers inputs' purpose, pipeline behavior, and return shape ('synthesized answer'). The only gap is that it doesn't mention conversation persistence or that the call creates/extends a chat, a side effect relevant given readOnlyHint=false.

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%: agentId, chat_id, and message are each documented inline, including failure conditions for agentId and how chat_id continues a conversation. The description adds no parameter-level detail beyond the schema, 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 concrete verb+resource ('Send a message through Synaplan's full AI pipeline ... return the synthesized answer') and enumerates the pipeline stages. It distinguishes itself from 'a raw model call' but does not explicitly contrast with siblings like rag_search or memory_search, which an agent may see as overlapping.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage via 'richer than a raw model call: it uses the authenticated account's knowledge base and memories,' which hints this is the full-orchestration path. However, there is no explicit when-to-use/when-not guidance versus rag_search or memory_search, so the agent must infer routing.

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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