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Quillm

by ai.quillm
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Leave notes for future agents

write_notes
DestructiveIdempotent

Saves durable notes for future agents (including yourself in a later session, with no memory of this one). Use it to record what is NOT obvious from the code: where the data comes from and how to refresh it (queries, API calls, file paths), definitions ("MRR excludes one-off invoices"), and user preferences ("Kev wants EUR, weekly granularity"). Notes attach to a view, a dataset, or the whole workspace, and are shown in get_workspace / get_view. Replaces the previous notes for that target, so include what should be kept.

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": {
      "target_type": "<target_type>",
      "notes": "<notes>"
    },
    "name": "write_notes"
  }
}

Input Schema

NameRequiredDescriptionDefault
notesYesMarkdown. Replaces existing notes for this target.
targetNoView slug or dataset name. Omit for workspace notes.
target_typeYesWhat the notes attach to.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations flag destructiveHint=true and idempotentHint=true, and the description earns its keep by explaining the actual destruction semantics: notes REPLACE the previous notes for that target, so existing content must be re-included. It also discloses where notes surface (get_workspace / get_view), which annotations cannot convey.

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?

Four tightly packed sentences, front-loaded with the action and the recording criteria, followed by scope and the destructive-replace caveat. The parenthetical about a later session with no memory justifies itself by explaining why notes matter at all.

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?

With no output schema and a small 3-parameter surface, the description covers everything needed: what to write, where it attaches, where it is read back, and the overwrite hazard. An agent can call this correctly without further context.

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 description coverage is 100%, so the baseline is 3, but the description adds real semantic value: it tells the agent what content belongs in `notes`, and it maps the three targets (view, dataset, workspace) to the concept of note attachment. The replace-behavior of `notes` is reinforced beyond the schema's one-line mention.

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?

States a specific verb and resource ('Saves durable notes for future agents') and immediately scopes the content ('what is NOT obvious from the code'). No sibling tool does anything similar, so the agent can distinguish it from update_view/get_view 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?

Gives concrete, actionable guidance on what to record: data provenance and refresh mechanics, definitional conventions, and user preferences, each with an example. It lacks an explicit 'when not to use this' exclusion, but the 'NOT obvious from the code' framing implicitly bounds usage.

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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