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mp-juns

universal-research-mcp

by mp-juns

research_commit_ingest

Commit one approved pending draft to the canonical ledger and update derived indexes. Requires draft ID, SHA256, and approval receipt to prevent replay.

Instructions

Append exactly one approved pending draft and refresh derived indexes.

This is a mutating, non-idempotent host-approved tool. It accepts no record body and no model-supplied approval boolean. It refuses a replay, any change to the canonical ledger or staged source files, or an invalid human scope approval. Canonical append success is reported separately from derived-index refresh status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draft_idYes
draft_sha256Yes
approval_receipt_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations declare readOnlyHint: false, idempotentHint: false. The description explicitly confirms it is mutating and non-idempotent, adds that it is host-approved, accepts no record body, reports success separately for append vs. index refresh, and refuses replay. This fully compensates for the lack of annotations-driven behavioral details.

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?

The description is compact (four sentences) and front-loaded with the core action. Every sentence adds important behavioral or usage constraints. It is efficient but could be slightly more structured (e.g., bulleted constraints), but remains clear and uncluttered.

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 mutation nature and the presence of an output schema, the description covers the main behavioral aspects: what it does, what it refuses, and how results are reported. It lacks explicit mention of error scenarios or return values, but the output schema reduces the need for that detail.

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 0%, so the description must add meaning. It explains that draft_id, draft_sha256, and approval_receipt_id correspond to an approved pending draft and its approval receipt—tying them together contextually. It does not describe each parameter's format or constraints, but enough context is given for correct invocation.

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 clearly states it appends exactly one approved pending draft and refreshes derived indexes—a specific verb and resource. This distinctively separates it from siblings like research_prepare_ingest or research_pending_ingest_status, which handle draft preparation or status queries.

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?

The description explicitly mentions constraints: it requires an approved pending draft, refuses replays, invalid approval, or modifications to the canonical ledger. It does not pair with any sibling tool, but the context of when-not-to-use (replay, invalid approval) is clear.

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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