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Macula MCP

by macula-io
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TypeScript

mesh_remember

Save conversational snippets to shared mesh memory for later search and recall by any agent in future sessions.

Instructions

Deposit something worth remembering into the mesh's shared memory (mcl-rag) -- one mesh RPC (add_knowledge), so it becomes searchable via mesh_recall for any agent, not just you, in future sessions. Short deposits (a sentence or two) are fine -- unlike raw document ingestion, this is designed for conversational snippets and won't silently produce zero chunks. Be deliberate about what you write here: this is shared, not private to you, and this mesh doesn't encrypt payloads -- the same caveat mesh_say and mesh_open_room already carry. Don't deposit anything you wouldn't want another agent or operator reading.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicsNoTopic labels to tag this deposit with, for later topic-filtered search.
contentYesThe text to remember, in your own words. Markdown is fine -- header-aware chunking splits it if long.
source_labelNoGrouping/attribution label, e.g. "agent-notes/macula-mcp-presence". Defaults to "conversational" if omitted.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.37.0
    • removedInput schema / properties / host
      Removed value: -{
-  "description": "Station to connect through for both the discovery lookup and the call, \"host[:port]\". Defaults to station-de-frankfurt.macula.io:4433.",
-  "type": "string"
-}
  2. First observedv0.28.7

TDQS

A4.3/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden and does so well: shared-not-private scope, no payload encryption, cross-session/any-agent visibility via mesh_recall, and the guarantee that short deposits won't silently produce zero chunks. These are exactly the behavioral traits an agent needs before writing to a persistent shared store.

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?

Front-loaded with the action and its consequence, and each sentence adds a distinct piece of information. It is on the long side and restates the privacy caveat twice (shared/no-encryption and 'what others can read'), which is some redundancy.

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 3-param write tool with no output schema, the description covers scope, persistence, retrieval path, and safety caveats completely enough to call it correctly. It stops short of describing failure modes or confirmation behavior, which would be the remaining gap.

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%, so topics, content and source_label are already documented in the schema. The description adds no extra constraint or format detail about the parameters themselves, so the baseline 3 applies.

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?

Opens with a specific verb+resource ('Deposit ... into the mesh's shared memory (mcl-rag)') and immediately names the retrieval counterpart mesh_recall, so an agent can distinguish write from read without opening any schema. It also states the underlying RPC (add_knowledge), grounding the purpose concretely.

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?

Gives clear context: designed for short conversational snippets rather than raw document ingestion, and warns to be deliberate because the store is shared. It does not explicitly rule out or route to the sibling mesh_remember_directory, which keeps it 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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