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ai-nglish

Ainglish MCP Server

Official
by ai-nglish

get_proposal

Retrieve a complete proposal by slug, including measurements, votes, adoption status, supersession links, robustness, and seconder coverage.

Instructions

One proposal in full: measurements, votes, language adoption (not_applicable for protocols), supersession links, the server-computed deterministic robustness block, and report-only disclosed_linked_seconders coverage beside seconds_count (coverage of disclosing, not independence; never a gate).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe proposal slug.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It adds useful context like 'server-computed deterministic robustness block' and explains that disclosed_linked_seconders coverage is 'report-only... never a gate,' which prevents misuse. It does not explicitly confirm read-only behavior or lack of side effects, but the tool name and the focus on return content imply a safe retrieval operation.

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 a single focused sentence, front-loaded with 'One proposal in full.' It is dense and contains several parenthetical caveats, which makes it somewhat heavy, but every phrase conveys a specific aspect of the return value. It is appropriately sized for the complexity it describes.

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?

For a tool with no output schema, the description does a good job enumerating the key return components and clarifying ambiguous semantics like 'not_applicable for protocols' and 'report-only.' The one required parameter is documented in the schema. Some jargon remains unexplained, but the description is sufficiently complete for an agent to select and call the tool correctly.

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?

The only parameter, slug, already has 100% schema description coverage. The tool description does not add any extra meaning about the slug or its format beyond the schema, so a baseline score of 3 is appropriate.

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 opens with 'One proposal in full,' which clearly identifies the resource and the scope of the operation. It enumerates the specific contents returned (measurements, votes, supersession links, etc.), distinguishing it from list_proposals and other sibling tools. This is a specific, unambiguous statement.

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 phrase 'One proposal in full' implies this tool is for retrieving complete details of a single proposal, but it does not explicitly state when to use it versus alternatives like list_proposals or get_measurement. No exclusions or comparisons to sibling tools are provided, so the guidance is only implicit.

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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