Skip to main content
Glama
mp-juns

universal-research-mcp

by mp-juns

research_prepare_ingest

Validate research inputs and create an immutable pending draft for ingestion, requiring source registrations to match a pre-approved study and kind from a human approval record.

Instructions

Validate input and create an immutable pending draft, never a canonical record.

Each new source registration contains only path, source_id, and source_type. The path must be project-contained and is hash-bound at preparation and commit. A canonical human approval record must already exist and cover this record's study and kind.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordYes
approval_refYes
source_registrationsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations provide no behavioral hints (all false), so the description carries the full burden. It discloses that the tool creates an immutable draft, validates input, and hash-binds paths. However, it does not specify error behavior, side effects of calling repeatedly, or the impact of missing prerequisites, leaving important behavioral gaps.

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 three sentences with the core purpose front-loaded. It efficiently conveys validation, creation, immutability, and the prerequisite constraint. Minor inefficiency: the second sentence about source registration format could be more tightly integrated, but overall it avoids unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three parameters and a prerequisite, the description covers the main behavioral contract and a key constraint. However, it omits explanation of the required 'record' parameter and the return value (output schema is not shown but context indicates it exists). This leaves gaps for an agent to fully understand invocation expectations.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain parameters. It only partially describes source_registrations by listing expected fields (path, source_id, source_type) and hints that approval_ref references the prerequisite approval. The required 'record' parameter is completely unexplained, and no details are given about optional source_registrations structure beyond the three fields.

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 the tool validates input and creates an immutable pending draft, distinguishing it from final record creation. It explicitly notes 'never a canonical record', differentiating from siblings like research_commit_ingest which likely finalizes the draft.

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

Usage Guidelines3/5

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

The description implies this tool is used before a commit step by mentioning the draft is pending. It specifies a prerequisite ('canonical human approval record must already exist') but does not provide explicit when-to-use or when-not-to-use guidance or mention alternatives beyond the context of sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mp-juns/universal-research-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server