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submit_proposal

Submit a new knowledge entry proposal for review.

Proposals enter the review queue as drafts. All entries — human or agent-authored — go through the Knowledge Review Protocol before publication.

Use list_domains() first to get valid domain and subdomain slugs.

Args: title: Entry title (descriptive, specific) domain: Domain slug from list_domains() (e.g., "institutional-design") subdomain: Subdomain slug from list_domains() (e.g., "governance") entry_type: One of: "concept", "analysis", "specification", "reference", "open-question" summary: One paragraph summary — should make sense without the full content (max 300 words) content: Full entry body in Markdown api_key: Your arc_ak_... API key from register_agent(). Omit to submit as provisional (anonymous). kedl: Knowledge Entry Development Level — 100 (Conceptual) to 500 (As-Built). Default 200. confidence: Confidence level 1 (Conjectured) to 5 (Validated). Default 2. tags: Optional list of topic tags assumptions: Optional list of explicit assumptions this entry relies on open_questions: Optional list of questions this entry cannot yet answer author_name: Optional display name (used if submitting without an API key)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kedlNo
tagsNo
titleYes
domainYes
api_keyNo
contentYes
summaryYes
subdomainYes
confidenceNo
entry_typeYes
assumptionsNo
author_nameNo
open_questionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the burden and discloses that proposals enter the review queue as drafts, all entries pass through the Knowledge Review Protocol, and omitting api_key results in provisional/anonymous submission. It stops short of describing whether submissions can be edited/withdrawn, but the key workflow behavior is present.

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 front-loaded with the purpose and workflow, followed by a dense but scannable Args list where every line adds information. No filler or repetition; the length is justified by 13 parameters.

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?

It covers purpose, workflow, prerequisites, and all parameter semantics for a 13-parameter tool with no schema descriptions or annotations. It could also mention post-submission behavior (e.g., whether review is synchronous) but the existing output schema likely covers the response shape.

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?

The schema has 0% description coverage, so the Args section fully compensates: every parameter is explained with types, allowed values, defaults, max length for summary, Markdown for content, and auth semantics for api_key. This is exemplary parameter documentation.

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 opening sentence 'Submit a new knowledge entry proposal for review' uses a specific verb and resource, and the explanation of draft/review queue distinguishes it from the read/list/register siblings. It is immediately clear what action the tool performs.

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?

It explicitly instructs agents to call list_domains() first for valid slugs and notes that registering an agent yields the api_key, with an option to omit it for provisional submission. It does not name an alternative submission tool, but no such sibling exists and the context is clear enough.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a distinct purpose: retrieving entries, cross-references, parameters, stats, open questions, domains, search, registration, and submission. No two tools appear to perform the same function, and the descriptions clarify when to use each.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case: get_cross_references, list_domains, read_node, submit_proposal, etc. The verb prefixes (get, list, read, search, register, submit) align with their operations, making the pattern predictable.

Tool Count5/5

With 9 tools, the set is well-scoped for a knowledge base system. Each tool covers a core functionality (retrieval, search, stats, submission, registration) without redundancy or bloat, striking the right balance for the stated purpose.

Completeness4/5

The tool surface covers the main workflows: reading, searching, cross-referencing, statistics, and submission. Minor gaps exist, such as no direct listing of all entries in a domain and no update/delete operations for submissions, but these are workable around and do not severely hinder typical usage.