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io.github.norrietaylor/distillery-mcp

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by norrietaylor

distillery_store_batch

Batch import multiple knowledge entries in one call; valid entries persist, per-item failures reported. Use for bulk migrations or backfills where dedup isn't needed.

Instructions

Batch-store multiple knowledge entries in one call (no dedup/conflict checks).

USE WHEN: bulk-importing entries (e.g. GitHub history sync, migration, backfill) where per-entry dedup is unnecessary and throughput matters.

PARAMS:

  • entries (list[dict], required): List of entry dicts. Each must have:

    • content (str, required): The knowledge content.

    • author (str, required): Who authored this entry.

    • entry_type (str, optional, default="inbox"): Entry classification. Valid: [session, bookmark, minutes, meeting, reference, idea, inbox, github, person, project, digest, feed].

    • tags (list[str], optional): Tags for categorisation.

    • metadata (dict, optional): Arbitrary key-value metadata.

    • source (str, optional, default="claude-code"): Origin of the entry.

    • project (str, optional): Per-entry project override.

  • project (str, optional): Default project applied to entries lacking one.

RETURNS (success): { entry_ids: list[str | None], # per-item ids; null for failed items count: int, # number actually persisted results: list[dict], # per-item status preserving input order }

  • Successful items: { entry_id, persisted: true, dedup_action: "stored" }

  • Failed items: { entry_id: null, persisted: false, error: { code, message, details? } } Validation failures on individual items no longer abort the batch — valid entries are persisted and failures are reported per item in results (issue #364). Iterate results to discover failures. RETURNS (error): { error: true, code: "INVALID_PARAMS" | "BUDGET_EXCEEDED" | "INTERNAL", message: "..." } Top-level error is returned only for schema-level problems (entries not a list, budget exhaustion, persistence failure).

RELATED: distillery_store (single entry with dedup/conflict checks), distillery_watch (add feed sources with optional history sync)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entriesYes
projectNo
Behavior5/5

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

With zero annotations, the description carries the full burden, and it delivers: explains batch semantics (no dedup/conflict), per-item failure handling (valid entries persisted, failures reported in 'results'), top-level error conditions, and the return shape. It also mentions budget exhaustion and issue #364, showing up-to-date behavioral detail.

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 long but modularly structured with clear sections (USE WHEN, PARAMS, RETURNS, RELATED). Every sentence adds value—the length is warranted by the tool's complexity. Front-loading the purpose and using headers makes it scannable and efficient.

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?

For a tool with no annotations, no output schema, and a complex per-item batch API, the description covers all essential aspects: usage context, parameter specifications, success and error return formats, and related tools. An agent has everything needed to invoke it correctly without ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description provides exhaustive detail for each entry field: content, author, entry_type with valid values, tags, metadata, source, project, plus defaults. It also explains the top-level 'project' parameter. This fully compensates for the sparse schema.

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 opening line 'Batch-store multiple knowledge entries in one call (no dedup/conflict checks)' clearly states the verb, resource, and key distinguishing feature. It immediately contrasts with single-entry storage, making the purpose unmistakable.

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?

Includes a dedicated 'USE WHEN' section specifying exact scenarios (bulk import, migration, backfill) and the trade-off (no dedup, throughput matters). Also names related tools (distillery_store, distillery_watch) and their differentiation, fully routing the agent to the correct alternative.

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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