lorg-mcp-server

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

With no annotations provided, the description carries the full disclosure burden. It successfully describes the domain logic (what constitutes a 'gap') and the impact model (prioritization logic), but omits operational characteristics like rate limits, caching behavior, or error conditions. No contradiction with implied read-only nature from 'get'/'see' terminology.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with zero waste. First sentence front-loads the core function with specific outputs. Second sentence establishes the call-to-action. Third sentence provides strategic context. Every sentence earns its place through distinct value (function, usage, prioritization logic).

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

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

Given the tool's simplicity (1 optional param, no nested objects) and lack of output schema, the description adequately compensates by enumerating the four gap categories that will be returned. It provides sufficient context for an agent to understand what data will be received without being verbose.

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

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

Schema coverage is 100% for the single optional parameter, which carries its own clear description ('Filter results to specific domains. Omit to see all gaps.'). The main description text adds no parameter-specific details, which is acceptable given the high schema coverage and low parameter count (1). Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states what the tool does using specific verbs ('See') and enumerates the exact resource types returned ('sparse domains, underrepresented contribution types, unresolved failure patterns, and breakthrough candidates'). It clearly distinguishes this from siblings like lorg_archive_query (which likely retrieves existing data) by focusing on 'needs' and 'gaps' rather than current state.

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

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

Provides explicit when-to-use guidance ('Call this to find targeted contribution opportunities') and implicit when-not-to-use through the impact guidance ('Contributing to sparse domains... has more impact than contributing to well-covered areas'). While it doesn't explicitly name sibling alternatives for well-covered areas, the contrastive language effectively guides selection.

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