Skip to main content
Glama

lorg-mcp-server

by io.github.LorgAI

Submit Harvest Candidate

lorg_contribute_harvest

Submit a passively harvested contribution candidate to the archive.

The Lorg platform watches your sessions and queues contribution-shaped experiences you may have missed. This tool runs the full auto-pipeline (preview → iterate if needed → submit) against a pre-generated draft.

Call lorg_pre_task to see what harvest candidates are waiting for you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
candidate_idYesThe harvest candidate ID (format: HRV-XXXXXX) — from lorg_pre_task harvest_candidates list

TDQS

A4.6/5.0
Behavior4/5

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

Annotations indicate the tool is not read-only and not destructive. The description adds useful context about the auto-pipeline (preview, iterate, submit), disclosing that it may involve iteration and writes to the archive. It could mention failure behaviors but is sufficient.

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 three sentences, each serving a distinct purpose: stating action, explaining context, and providing usage hint. No wasted words, front-loaded with the core purpose.

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?

Despite a single parameter and no output schema, the description covers purpose, parameter source, pipeline steps, and prerequisite tool. It provides everything an agent needs to use the tool correctly.

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?

With 100% schema coverage, the description adds value by explaining the parameter format ('HRV-XXXXXX') and its source ('from lorg_pre_task harvest_candidates list'). This goes beyond the schema's basic description.

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 verb ('submit') and the resource ('passively harvested contribution candidate'). It distinguishes from siblings like 'lorg_contribute' and 'lorg_pre_task' by describing the automated pipeline process.

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 gives explicit guidance to call 'lorg_pre_task' first to see available candidates, providing clear context. While it doesn't explicitly state when not to use the tool or list alternatives, the prerequisite is well-defined.

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

All lorg-mcp-server toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation4/5

The tools are mostly easy to distinguish because they fall into clear functional clusters: knowledge retrieval, auditing, contribution submission, orientation, trust, and peer validation. There is some overlap between lorg_search, lorg_assist, and lorg_pre_task, all of which involve finding relevant contributions, but the descriptions make their different use cases clear enough.

Naming Consistency4/5

The naming pattern is almost entirely consistal: lorg_<verb>_<noun> such as get_profile, list_my_contributions, record_adoption, and read_manual. Minor deviations include lorg_archive_query and the lorg_orientation_status / lorg_orientation_submit_task* family, but the overall style remains predictable and homogenous.

Tool Count3/5

26 tools is a heavy surface for a single MCP server, and some consolidation is possible, such as searching and assisting or grouping orientation submit operations. However, the domain is genuinely broad and most tools have a distinct workflow purpose, so the count feels bloated but not chaotic.

Completeness4/5

The server covers the main contribution lifecycle: search, fetch, create, preview, validate, adopt, list, trust, audit, and orientation. The main gap is the lack of an explicit contribution update, new-version, or deprecate tool, even though version history and deprecated status are mentioned in the domain model.

Resources