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Quillm

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Read a view

get_view
Read-onlyIdempotent

Returns one view in full: metadata, agent notes, revision history, the schema of every dataset it reads, and its React source code. Always call this before update_view so your edits match the current source exactly.

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": {
      "slug": "<slug>"
    },
    "name": "get_view"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYesThe view's slug, as listed by get_workspace, or its page URL.
revisionNoReturn the source of an older revision instead of the newest one.
include_sourceNoDefault true. Set false to get only metadata, notes and dataset schemas.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered structurally. The description adds genuinely useful context beyond that: the payload includes revision history and full React source, and it hints at the read-before-write discipline required for safe edits. No auth or rate-limit detail, but the bar is lower with annotations present.

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 filler: the first front-loads the return payload, the second front-loads the actionable workflow rule. Every clause earns its place.

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?

With no output schema, the description carries the burden of describing return content and does so concretely across all five payload parts, plus the read-before-update obligation. Missing only edge-case behavior (nonexistent slug, stale revision errors) to be fully complete.

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%, so slug, revision, and include_source are already documented at the field level. The description's 'one view in full' and 'React source code' only loosely reinforce the include_source/revision semantics without adding format or default details the schema lacks. Baseline 3 is correct.

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?

Specific verb and resource ('Returns one view') plus an explicit enumeration of what is returned: metadata, agent notes, revision history, dataset schemas, and React source. That enumeration distinguishes it from siblings like get_workspace (which lists views) and check_view (which presumably validates rather than returns).

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 states a concrete workflow rule — 'Always call this before update_view so your edits match the current source exactly' — which is exactly the kind of routing guidance an agent needs. It stops short of naming when NOT to use it (e.g. when check_view would suffice), so it is clear context without full exclusions.

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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