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by WenyuChiou
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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autofill_apply

Apply AI-authored autofill sections to update paper notes. Consumes autofill_emit JSON to fill summaries in specified clusters.

Instructions

Apply AI-authored autofill sections to paper notes. Consumes autofill_emit JSON and updates notes. When to use: after an AI returns summaries for an autofill prompt. When NOT to use: to generate prompts; use autofill_emit instead. Args: cluster_slug: cluster to update; scored: list or {"papers": [...]}. Returns: keys cluster_slug, candidate_count, filled, skipped, missing, error. Example: >>> autofill_apply("my-topic", {"papers": [{"slug": "paper-1"}]}) {"cluster_slug": "my-topic", "filled": []}

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": {
      "cluster_slug": "<cluster_slug>",
      "scored": "<scored>"
    },
    "name": "autofill_apply"
  }
}

Input Schema

NameRequiredDescriptionDefault
cluster_slugYes
scoredYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are provided, so the description must carry the full burden. It describes that the tool updates notes and consumes autofill_emit JSON, and lists return fields with an example. However, it does not detail the extent of updates (e.g., overwrite/append) or side effects, leaving some ambiguity. Still, it is fairly transparent.

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?

The description is concise with minimal fluff: a one-sentence purpose, usage guidelines, parameter explanations, return fields, and an example. It is front-loaded and each sentence serves a clear purpose.

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?

Given the tool's complexity (2 params, no output schema provided) the description covers the main use case, lists return fields, and gives an example. However, the example is somewhat ambiguous (empty filled list) and does not clarify behavior for different 'scored' formats. Still, it is largely complete.

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?

With 0% schema description coverage, the description manually explains each parameter: 'cluster_slug: cluster to update; scored: list or {"papers": [...]}'. This adds meaning beyond the raw type definitions, compensating for the lack of schema descriptions.

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 starts with a specific verb 'Apply' and resource 'AI-authored autofill sections to paper notes'. It clearly distinguishes from sibling 'autofill_emit' by stating it consumes its JSON output, ensuring the agent uses the correct tool.

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

Usage Guidelines5/5

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

The description explicitly states when to use ('after an AI returns summaries for an autofill prompt') and when NOT to use ('to generate prompts; use autofill_emit instead'), including the name of the alternative. This provides clear guidance.

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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