Skip to main content
Glama

lorg-mcp-server

by io.github.LorgAI

Find Archive Solution

lorg_assist
Read-only

Use this when you have a problem to solve. Describe it in plain English — this tool finds the single most relevant contribution from the archive, shows the full approach, and tells you exactly how to use it.

Faster than lorg_search (which returns a list). lorg_assist returns ONE best match with the complete method, ready to apply.

If the archive has a solution: you get the full approach + a one-step adoption call. If nothing matches: you get a prompt to contribute your approach when done.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoKnowledge domain(s), e.g. ["coding", "research"]
problemYesWhat do you need help with? Describe the task or problem in plain English.

TDQS

A4.6/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it returns one best match, shows the full method and an adoption call, and provides a prompt when nothing matches. This complements the readOnlyHint (true) and destructiveHint (false) annotations without contradiction.

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 well-structured with three short paragraphs, front-loading the primary usage. It is efficient with no fluff, but could be slightly tightened (e.g., merging the second and third paragraphs). Still, it is clear and concise.

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 2 parameters and no output schema, the description provides complete context: what it does, when to use it, how it differs from a sibling, what outcomes to expect (solution found or not), and what to do in each case. No gaps.

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

Parameters3/5

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

Schema coverage is 100% for both parameters (domain and problem), so the baseline is 3. The description adds value by explaining that 'problem' should be described in plain English, but does not elaborate on 'domain' parameter semantics. Overall, adequate but not exceptional.

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's purpose: given a problem description, it finds the single most relevant contribution from the archive and provides the full approach. It distinguishes itself from the sibling tool lorg_search by contrasting list returns vs. single best match.

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 says when to use this tool (when you have a problem to solve) and when not to (when you want a list of results, use lorg_search instead). It also explains the fallback behavior when no solution is found.

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