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Niche — Editorial Intelligence

by com.nicheangle

Intelligence query

niche_intelligence_query

Niche (nicheangle.com) research and analysis: answer an analyst-shaped question over fresh-scanned sources and get an intelligence answer as the deliverable, not a single post. Use for: 'the 10 biggest developments in X this week', 'what's emerging before it goes mainstream', 'where is investment activity rising', 'find 3 non-obvious narratives to publish on LinkedIn'. The answer is the ranked slate plus engine-grounded narratives or patterns: every narrative cites real slate stories and is fact-verified by a second pass (no source, no narrative). This uses the same engine as the Niche web app, so both surfaces return the identical grounded answer; do not synthesize your own narratives over the slate, present these.

Non-blocking: returns a session_id immediately (under 2s). Poll niche_session_state every ~3-5s. At status == cp1_awaiting_story the ranked slate (stories[]) is ready; present it right away. Synthesis runs concurrently and usually lands 20-90s after the slate (hard cap ~2 min); if you requested it, call niche_session_state(wait:30, wait_until:'synthesis') and repeat until synthesis_pending == false (usually 1-3 calls); don't give up early, you'll always get synthesis[] or a synthesis_shortfall_note. With synthesis:'none', synthesis stays null and synthesis_pending is false at cp1, so stop there. To turn a narrative into a post, pick its supporting story id and call niche_angle_propose; no new scan needed. BRAND: omit brand_id and a single/default brand binds automatically; on a MULTI-brand account you'll get brand_choice_required with brand_options[] (the query still runs). Ask the user which brand before drafting. Pass brand_id to bind one, or 'none' for unbranded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lensNoRanking posture. 'mainstream' (default) = authority-weighted. 'emerging' = inverts saturation to surface low-coverage, pre-mainstream signal. 'investment' = lifts stories carrying funding/raise/round/term-sheet markers.
countNoHow many developments / narratives to return (3-15). Default ~5-10.
windowNoRecency window: '24h' | 'week' | 'month' | 'quarter' | 'year'. 'this week' maps to 'week'. Overrides the niche's default recency. A strong bias by default; pair with recency_strict for a hard cutoff.
subjectYesThe subject/space to investigate (2-200 chars). Specific is better.
brand_idNoBinds this brand's voice, colors, offer, and CTA to the piece. Omit to use your default brand; on a multi-brand account pass the slug explicitly so a post about one product is not bound to another brand's identity. niche_whoami lists your brands.
platformNoOptional publish target (linkedin / x / instagram) that shapes each narrative's publish_hook.
synthesisNo'narratives' = N non-obvious publishable threads across the slate. 'patterns' = the named movement (pairs with lens:'investment'). 'none' (default) = ranked slate only, no synthesis.
recency_strictNoWhen true, `window` is a hard cutoff (out-of-window sources dropped before clustering) so 'nothing older than yesterday' is exact. Default false (bias only). Strict returns fewer, higher-confidence stories; use when precision matters more than breadth.
source_qualityNoSource-quality filter (niche-relative). 'strict' drops uncorroborated single-source silos that aren't primary/official or a niche authority, for high-trust answers only. 'balanced' (default) down-weights weak sources without dropping. 'broad' surfaces everything (incl. low-coverage emerging clusters), authority as a tiebreaker only.balanced
target_outputsNoOptional. The draft cells produced if you later draft a narrative into content (same cell list as niche_signal_scan, e.g. ['linkedin:image_post', 'x:thread']). Without this, a draft defaults to a single 'long_form_article'. Set it when you know the surfaces you want, so niche_draft_create yields them directly instead of needing niche_add_output after.
idempotency_keyNoOptional. Stable key so a retry reuses the original run instead of billing a second; an identical query fired while one is still running is auto-deduped regardless.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only provide readOnlyHint=false, openWorldHint=true, destructiveHint=false, so the description carries the full burden of explaining side effects and lifecycle. It adds rich behavioral detail: non-blocking session_id return, polling cadence, cp1_awaiting_story status, synthesis timing with a hard cap, and the synthesis:'none' branch. This goes far beyond the structured annotations and contains no 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 long but well-structured into purpose, async workflow, and brand handling. The opening sentence front-loads the core purpose, and each subsequent paragraph addresses a distinct aspect (polling, synthesis, brand binding, handoff to niche_angle_propose). Every sentence earns its place for this complex tool, though the web-app equivalence sentence could be trimmed without losing essential guidance.

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?

With no output schema, the description must explain return values and state transitions, and it does so explicitly: session_id, cp1_awaiting_story, stories[], synthesis[], synthesis_shortfall_note, brand_choice_required, and brand_options are all named. It covers the entire lifecycle from submission through polling to handing off to niche_angle_propose. The only minor omission is error/rate-limit behavior, which is not critical given the detailed workflow guidance.

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?

Input schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds brand_id semantics not fully captured in the schema: omitting brand_id auto-binds a single/default brand, multi-brand accounts return brand_choice_required with brand_options, and 'none' is a valid unbranded value. It also clarifies the behavioral effect of synthesis:'none', adding value beyond the schema.

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 a specific verb+resource: 'research and analysis: answer an analyst-shaped question over fresh-scanned sources and get an intelligence answer as the deliverable, not a single post.' It lists concrete use cases and explicitly contrasts with single-post tools, distinguishing it from siblings like niche_angle_propose and niche_draft_create. The 'same engine as the Niche web app' note further clarifies its scope.

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 'Use for:' and gives four analyst-style example questions, making the intended context unmistakable. It also tells the agent what not to do ('do not synthesize your own narratives over the slate, present these') and describes the follow-up path to niche_angle_propose. This is strong when/when-not guidance, even though it doesn't name an alternative for the initial query.

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
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