Skip to main content
Glama

Niche — Editorial Intelligence

by com.nicheangle

Ingest a voice profile

niche_voice_profile_ingest

Extract voice primitives (register / sentence rhythm / lexicon preferences / punctuation habits) from post-shaped text and persist onto the user's VoiceProfile. The voice primitives thread into content generation so generated copy matches the user's actual writing voice.

Two input shapes: pass posts (list of pre-collected text snippets, ≥80 chars each) or pass url (the server scrapes post-shaped snippets from the page: Substack / Medium / blog / X profile). Inline posts win when both are given. Inline post-shaped snippets need to be the user's own writing, not press articles or marketing copy.

Returns the extracted primitives + a diff of what changed on the stored VoiceProfile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoURL to scrape post-shaped text from. Substack / Medium / blog homepages work best; X / LinkedIn profile pages are supported but yield less per-snippet text.
postsNoPost-shaped text snippets the user wrote. ≥80 chars each; 3-10 snippets is the sweet spot for primitives extraction.
brand_idNoWhich brand's voice to ingest into. Voice is brand-scoped: omit for the default brand, or pass a brand_id (from niche_brand_profile_get) to set up that brand's voice without touching another brand's. A brand with no voice of its own falls back to the default voice at generation time.
archetypeNoOptional label for how the profile was acquired; tags the VoiceProfile.archetype field. Defaults to 'agent_ingest'.agent_ingest
overwriteNoWhether to replace an existing VoiceProfile. Default false: first run wins, subsequent runs are no-ops unless explicitly opted in, so an automated re-run does not overwrite a hand-curated voice.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=false, destructiveHint=false), the description discloses that this is a persist operation, explains the no-op default behavior ('first run wins, subsequent runs are no-ops'), describes server-side scraping for URLs, and notes the return of a diff of changes. This adds substantial behavioral context and does not contradict any annotation.

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 organized into three focused paragraphs: purpose, input shapes, and return value. It is front-loaded with the main action, and every sentence contributes meaningful information without redundancy or fluff.

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?

Given 5 parameters, useful annotations, and no output schema, the description covers the core purpose, both input modes, precedence, content quality, side effects (overwrite behavior), and return value. It provides enough context for an agent to select and invoke the tool correctly.

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

Parameters4/5

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

Schema coverage is 100% with detailed parameter descriptions, so the baseline is 3. The description adds value by explicitly stating the posts-vs-url precedence rule and explaining the brand-scoped behavior, which is not fully apparent from individual parameter descriptions.

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 'Extract voice primitives ... and persist onto the user's VoiceProfile,' identifying the specific verb, resource, and outcome. It distinguishes the tool from siblings like niche_brand_profile_get/set by focusing on voice ingestion from post-shaped text, and it explains the purpose ('thread into content generation').

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?

Provides explicit guidance on input shapes: pass `posts` or `url`, with precedence ('Inline posts win when both are given'). Also gives quality requirements ('≥80 chars each', 'user's own writing, not press articles or marketing copy'). However, it does not directly contrast with sibling tools such as niche_brand_profile_set, so explicit alternatives are not named.

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

All Niche — Editorial Intelligence 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

A4.2/5.0
Disambiguation4/5

Tools are mostly distinct across the pipeline—discovery, brand setup, drafting, rendering, publishing, session management—and descriptions are detailed. Minor overlap exists between niche_signal_scan and niche_intelligence_query (both discovery) and between niche_add_output and niche_draft_create (both generate outputs), but the descriptions clearly differentiate them. Overall, an agent can reliably select the right tool.

Naming Consistency3/5

All tools share the `niche_` prefix and snake_case, but the verb/noun order is inconsistent: some are verb-first (add_output, render_image_card, list_sessions) while many are noun-first (angle_propose, brand_kit_ingest, draft_create, session_cancel). This mixed convention requires the agent to learn each name individually rather than predict the pattern.

Tool Count3/5

25 tools is on the heavy side but appropriate for the platform's scope—full editorial intelligence from discovery to publish. No redundant tools, and each covers a distinct aspect, but the count is high enough that it feels dense and may require orientation (which niche_whoami addresses).

Completeness4/5

The toolset covers the full workflow: discovery, angle proposal, drafting, revision, publishing, rendering, asset attachment, brand setup, and session management. Minor gaps exist, such as no direct read tool for the current BrandKit (only via ingest status) and no hard-delete tool (session_cancel mentions the REST endpoint instead), but these are edge cases and agents can work around them.

Resources