# Zoning Signal (remote · zoningsignal.com)

US municipal zoning intelligence — corridor analysis, place dossiers, named-pattern detection.

- Trust score: 77/100 (medium)
- Change this week: +6
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `zoningsignal.com`: 77/100 (this document), [markdown](https://verifymcp.io/servers/com-zoningsignal-observatory/zoningsignal.md), [page](https://verifymcp.io/servers/com-zoningsignal-observatory/zoningsignal)

## Channel facts

- Endpoint: `https://zoningsignal.com/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `2.0.1`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 80/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 80/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 3869 tokens (~184/item across 21 items; 17 tools + 4 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http com-zoningsignal-observatory https://zoningsignal.com/mcp
```

### Codex

```toml
[mcp_servers.com-zoningsignal-observatory]
url = "https://zoningsignal.com/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-zoningsignal-observatory": {
      "type": "remote",
      "url": "https://zoningsignal.com/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add com-zoningsignal-observatory --url https://zoningsignal.com/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  com-zoningsignal-observatory:
    url: "https://zoningsignal.com/mcp"
```

### Other

```json
{
  "mcpServers": {
    "com-zoningsignal-observatory": {
      "type": "http",
      "url": "https://zoningsignal.com/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 76, +3)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 73, +1)

No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-29 (score 72, +1)

No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 71, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 70)

First indexed and scored.

## MCP tools (17)

### `describe_zoning_signal` (~57 tokens)

Describe Zoning Signal

Return the canonical product description for Zoning Signal — what the observatory is, the four artifact types it publishes, the regional scope of current coverage, and the methodology. Call once per session to ground subsequent tool calls in canonical context.

Output parameters:

- `artifact_types` (array)
- `built_by` (object)
- `description` (string)
- `methodology` (object)
- `name` (string)
- `regional_scope` (object)
- `surfaces` (object)

### `list_places` (~135 tokens)

List Places

List every place dossier (per-jurisdiction reading) the observatory publishes. Optionally filter by state. Returns city, state, slug, signal strength, signal direction, and the dossier URL. Use to discover the available place-level coverage before calling describe_place. Phase 12 — renamed from list_cities to align with the canonical content-type vocabulary (the loader function is getAllContent("place"); URLs are /places/{slug}; the describe tool is describe_place).

Input parameters:

- `state` (string): Optional US state name (e.g., "Florida") to filter the result set. Omit for all places across all states.

Output parameters:

- `cities` (array)
- `count` (number)

### `list_corridors` (~62 tokens)

List Corridors

List every published corridor page. A corridor is the cross-municipal economic-topology view — the cross-jurisdiction read on a shared infrastructure spine, aquifer, or commercial gravity field. Returns name, slug, constituent cities, primary axis, and URL.

Output parameters:

- `corridors` (array)
- `count` (number)

### `list_patterns` (~145 tokens)

List Patterns

List every named pattern in the Pattern Atlas. A named pattern is a coined recurring structure observed across multiple jurisdictions or multiple meetings (e.g., "The Quiet Revolution"). Returns slug, display name, canonical pattern URL (/patterns/{slug}, the DefinedTerm canonical home as of Phase 9), lifecycle stage, signal score, exhibits count, spatial scope, related briefs, and the voxel_lead. Use as the discovery surface for the Pattern Atlas; pair with describe_pattern for full dossier detail. Phase 12 — renamed from current_named_patterns to align with the canonical content-type vocabulary (loader: getAllContent("pattern"); URLs: /patterns/{slug}; describe tool: describe_pattern).

Output parameters:

- `count` (number)
- `patterns` (array)

### `describe_pattern` (~164 tokens)

Describe Pattern

Return the full dossier for a named pattern: voxel_lead, signal_status (score/direction/horizon/confidence/pips), scope (spatial/temporal/topical/corridors), full exhibits inventory with detection metadata, defensive responses, provenance chain, related briefs, related places, related corridors, audiences, and the canonical surfaces (dossier URL, DefinedTerm @id, DefinedTermSet @id, atlas list URL). Use when an agent needs the structured pattern data to cite or analyze. Each pattern is a citable entity in the corpus's entity graph; the DefinedTerm canonical home gives AI agents a stable reference.

Input parameters:

- `slug` (string, required): The pattern slug (e.g., "self-storage-canary"). Use current_named_patterns to discover available slugs.

Output parameters:

- `audiences` (array)
- `claims` (array)
- `defensive_response` (array)
- `exhibits` (array)
- `freshness` (object)
- `lifecycle_stage` (string)
- `name` (string)
- `named_at` (string)
- `provenance_chain` (array)
- `related_briefs` (array)
- `related_corridors` (array)
- `related_places` (array)
- `scope` (object)
- `signal_status` (object)
- `slug` (string)
- `surfaces` (object)
- `url` (string)
- `voxel_lead` (string)

### `list_entities` (~377 tokens)

List Entities

List every named entity in the Decoder Index — the smallest citable unit of authority in the corpus. Returns the four-class taxonomy (Person / Organization / Legislation / CreativeWork) with class-specific summary fields (jobTitle for Person; jurisdiction for Organization / Legislation / Project; legal_status for Legislation; case_number + work_status for Project) plus cross-reference counts (meetings_count, briefs_count, watches_count, patterns_count) for each entity. Filter by entity_class, place (jurisdiction), or search substring. Use as the discovery surface for the entity graph; pair with describe_entity for full structured detail. Each entity's schema_id is a stable cross-page reference (`/entities/{slug}#{class.toLowerCase()}`) that resolves to the canonical Schema.org node — Person / Organization / Legislation / CreativeWork — for AI-citation grounding.

Input parameters:

- `entity_class` (string): Filter by entity class. "Person" = board members, attorneys, applicants (individuals), elected officials. "Organization" = developer firms, law firms, agencies, HOAs, planning consultancies. "Legisla…
- `place` (string): Optional: filter to entities scoped to a specific place (e.g., "leesburg-florida"). Matches entities whose related_places, organization.jurisdiction, legislation.jurisdiction, or creative_work.jurisd…
- `search` (string): Optional case-insensitive substring search across display_name, canonical_role, voxel_lead, and slug. Use for natural-language entity discovery (e.g., "denial bloc", "intersection mitigation", "form-…

Output parameters:

- `by_class` (object)
- `count` (number)
- `entities` (array)
- `filters` (object)
- `surfaces` (object)

### `describe_entity` (~286 tokens)

Describe Entity

Return the full structured dossier for a named entity — the canonical citable artifact for any actor, organization, ordinance, or project the corpus references. Returns: voxel_lead (134-167 word voxel-disciplined identity prose), canonical_role, the class-specific cluster (person.voting_record for board members; organization.type + jurisdiction; legislation.legal_status + effective_date + sunset_date + citation; creative_work.work_type + status + case_number), the bidirectional graph references (appears_in_meetings, appears_in_briefs, appears_in_watches, exhibits_patterns, related_entities, related_places, related_corridors), the provenance_chain, and the canonical surfaces (dossier URL, schema_id, decoder_index_hub). Each schema_id (`/entities/{slug}#{class.toLowerCase()}`) is the stable cross-page Schema.org reference — Person / Organization / Legislation / CreativeWork — that AI agents resolve to when citing the entity. Use when grounding a citation, when reasoning about an entity's full role across the corpus, or when traversing the entity graph from a single name.

Input parameters:

- `slug` (string, required): The entity slug (e.g., "sb-180", "hanover-land-company", "anita-geraci-carver"). Use list_entities to discover available slugs. The Decoder Index hub at /entities lists every entity grouped by class.

Output parameters:

- `appears_in_briefs` (array)
- `appears_in_meetings` (array)
- `appears_in_watches` (array)
- `canonical_role` (string)
- `creative_work` (object)
- `display_name` (string)
- `entity_class` (string)
- `exhibits_patterns` (array)
- `last_active` (string)
- `legislation` (object)
- `named_at` (string)
- `organization` (object)
- `person` (object)
- `provenance_chain` (array)
- `related_entities` (array)
- `related_places` (array)
- `schema_id` (string)
- `slug` (string)
- `url` (string)
- `voxel_lead` (string)

### `meeting_index` (~132 tokens)

Meeting Index

Return meeting readings for a specific city across an optional date range. A meeting reading is a plain-English read of one harvested planning-board, council, or commission meeting, with signal extraction and entity mapping. Use to drill from a city or corridor into the temporal record.

Input parameters:

- `city` (string, required): City name (e.g., "Clermont"). Case-insensitive.
- `from_date` (string): Inclusive lower bound (ISO 8601 date). Omit to span back to the earliest reading.
- `to_date` (string): Inclusive upper bound (ISO 8601 date). Omit for the latest reading.

Output parameters:

- `city` (string)
- `count` (number)
- `from_date` (string)
- `meetings` (array)
- `to_date` (string)

### `list_meetings` (~171 tokens)

List Meetings

Return meeting readings across all cities, optionally filtered by date range or jurisdiction substring. Same response shape as meeting_index but with no required parameters — call with no args to get the full corpus, or pass a jurisdiction substring (e.g., "minneola") to filter by city without requiring an exact match. Use when you need to enumerate the full meeting record or scan across cities by date range.

Input parameters:

- `from_date` (string): Inclusive lower bound (ISO 8601 date). Omit to span back to the earliest reading.
- `jurisdiction` (string): Optional case-insensitive substring to filter by city (e.g., "minneola"). Omit for all cities.
- `to_date` (string): Inclusive upper bound (ISO 8601 date). Omit for the latest reading.

Output parameters:

- `count` (number)
- `filters` (object)
- `meetings` (array)

### `list_watch_items` (~236 tokens)

List Watch Items

Return The Watch — the field's forward calendar of pending events, scheduled hearings, regulatory sunsets, and condition-triggered milestones the observatory is tracking. Filter by status (pending / resolved / obsolete), horizon (imminent / near-term / structural), or scope (place / corridor / brief). Use to surface what the field is watching from any cognitive position.

Input parameters:

- `brief` (string): Optional: filter to items linked to a specific named-pattern brief.
- `corridor` (string): Optional: filter to items scoped to a specific corridor (e.g., "us-27-south-lake").
- `horizon` (string): Optional: filter to items in the named horizon band. Imminent = ≤14 days; near-term = ≤90 days; structural = >90 days or condition-triggered.
- `place` (string): Optional: filter to items scoped to a specific place dossier (e.g., "leesburg-florida").
- `status` (string): Filter by lifecycle status. Defaults to 'pending' (active watch items only); pass 'all' for the full corpus including resolved + obsolete entries.

Output parameters:

- `count` (number)
- `status` (string)
- `watch_items` (array)

### `get_track_record` (~246 tokens)

Get Track Record

Return the observatory's public calibration scorecard — the aggregate accuracy of past watch-item directional reads, horizon calls, and significance assessments across resolved watches. Returns: total_resolved, directional accuracy (aligned + 0.5 × mixed), horizon accuracy (within / total), significance accuracy (confirmed / total), per-confidence-pip stratification, recent resolutions, and per-jurisdiction breakdown. Optionally scope to a single jurisdiction or corridor's constituent set. Use when an agent or user wants to assess Zoning Signal's historical forecasting accuracy before citing a current prediction. Misreads are reported.

Input parameters:

- `brief` (string): Optional: scope to a brief slug (e.g., "six-month-board-flip"). Returns the track record for watches linked to a specific named-pattern brief.
- `corridor` (string): Optional: scope to a corridor slug (e.g., "us-27-south-lake"). Returns the aggregate track record across the corridor's constituent places.
- `jurisdiction` (string): Optional: scope to a single place slug (e.g., "leesburg-florida") for that city's track record only. Use list_cities to discover available slugs.

Output parameters:

- `by_confidence` (array)
- `directional` (object)
- `horizon` (object)
- `obsolete` (number)
- `pending` (number)
- `recent` (array)
- `scope` (object)
- `significance` (object)
- `surfaces` (object)
- `total_resolved` (number)

### `describe_place` (~238 tokens)

Describe Place

Return the dossier projection for a city, in the requested cognitive lens. Defaults to the synthesis projection (the multidimensional view that holds all lenses in superposition and names the dialectics). Pass a single-lens value to get the focused cognitive position — useful when the agent is acting on behalf of a user with a specific stake (developer underwriting, investor thesis, broker client argument, attorney precedent search, resident orientation, civic-leader regional coordination).

Input parameters:

- `lens` (string): The cognitive position to project. Defaults to "synthesis". Canonical lenses: developer, investor, broker, attorney, business, resident, civic-leader. Aliases route to canonical: legal/lawyer/counsel…
- `slug` (string, required): The place slug (e.g., "clermont-florida"). Use list_places to discover available slugs.

Output parameters:

- `available_lenses` (array)
- `claims` (array)
- `fell_back_to_synthesis` (boolean)
- `freshness` (object)
- `frontmatter` (object)
- `lens` (string)
- `lens_was_requested` (string)
- `projection` (string)
- `record_status` (object|null)
- `slug` (string)
- `title` (string)
- `type` (string)
- `url` (string)

### `describe_corridor` (~170 tokens)

Describe Corridor

Return the dossier projection for a corridor, in the requested cognitive lens. Same lens enum and default as describe_place. Corridor projections surface cross-municipal dialectics and shared-infrastructure dynamics that no single place dossier captures.

Input parameters:

- `lens` (string): The cognitive position to project. Defaults to "synthesis". Canonical lenses: developer, investor, broker, attorney, business, resident, civic-leader. Aliases route to canonical: legal/lawyer/counsel…
- `slug` (string, required): The corridor slug (e.g., "us-27-south-lake"). Use list_corridors to discover available slugs.

Output parameters:

- `available_lenses` (array)
- `claims` (array)
- `fell_back_to_synthesis` (boolean)
- `freshness` (object)
- `frontmatter` (object)
- `lens` (string)
- `lens_was_requested` (string)
- `projection` (string)
- `record_status` (object|null)
- `slug` (string)
- `title` (string)
- `type` (string)
- `url` (string)

### `describe_meeting` (~338 tokens)

Describe Meeting

Return the full dossier projection for a meeting reading, in the requested cognitive lens. Same lens enum and default as describe_place / describe_corridor — eight total projections (seven stakeholder lenses — developer, investor, broker, attorney, business, resident, civic-leader — plus synthesis as the default). Returns the lens-projected body, full frontmatter (jurisdiction, board, meeting_date, document_type, key_signals, vote tallies), citation-stable claims[] (per the Phase 11 Citable Contract; populates as meeting claim scopes graduate), four-clock freshness, and the structured record_status block (record_type / meeting_status / outcome_status / minutes_available / vote_final) — the last prevents agents from summarizing agenda intent as completed action. Use to ground citations in a specific meeting's reading; pair with list_meetings or meeting_index for discovery.

Input parameters:

- `lens` (string): Optional cognitive lens. Default: synthesis (the whole-picture multi-projection view). Canonical lenses: developer, investor, broker, attorney, business, resident, civic-leader. Aliases route to cano…
- `slug` (string, required): The meeting slug (e.g., "leesburg-pc-2026-01"). Use list_meetings or meeting_index to discover available slugs.

Output parameters:

- `available_lenses` (array)
- `claims` (array)
- `fell_back_to_synthesis` (boolean)
- `freshness` (object)
- `frontmatter` (object)
- `lens` (string)
- `lens_was_requested` (string)
- `projection` (string)
- `record_status` (object|null)
- `slug` (string)
- `title` (string)
- `type` (string)
- `url` (string)

### `describe_watch` (~200 tokens)

Describe Watch

Return the full dossier for a watch item — the observatory's forward-looking observation primitive. Returns title, subtitle, scope (place / corridor / pattern / brief / region), trigger (type / date / condition), significance (score / horizon / confidence / confidence_pips / why_it_matters_voxel), full body prose, four-clock freshness, and citation-stable claims[]. For RESOLVED watches, also returns the outcome cluster (outcome_type, outcome_summary, prediction_assessment with directional/horizon/significance assessments, lesson, citations) — and the lesson surfaces as a stable claim_id (per the Phase 11 Citable Contract × Phase 8 Resolution Bridge compound). Use to ground citations in a specific watch's prediction or resolution; pair with list_watch_items for discovery.

Input parameters:

- `slug` (string, required): The watch slug (e.g., "lake-bright-council-mar-23"). Use list_watch_items to discover available slugs.

Output parameters:

- `body` (string)
- `claims` (array)
- `freshness` (object)
- `outcome` (object)
- `scope` (object)
- `significance` (object)
- `slug` (string)
- `subtitle` (string)
- `title` (string)
- `trigger` (object)
- `type` (string)
- `url` (string)

### `submit_agent_feedback` (~444 tokens)

Submit Agent Feedback

Submit feedback to the observatory's operators about the MCP tool surface. The active counterpart to the passive invocation log. Categories: 'gap' (a capability you expected and didn't find), 'error' (an unexpected failure or wrong result), 'praise' (a tool or surface that did exactly what you needed), 'suggestion' (a refinement you'd recommend), 'citation_request' (a claim or fact you want surfaced with a stable @id you can cite). The submission auto-attaches the prior 10 invocations from your MCP-Session-Id, so operators read your feedback annotated with the call sequence that produced it — no need to repeat what you tried. Operators triage every submission and surface notable feedback at /agent-observatory. This is how the observatory evolves toward what agents actually need.

Input parameters:

- `about_tool` (string): Optional: the tool name this feedback is about (e.g., "describe_corridor"). Lets operators rollup feedback per tool.
- `about_url` (string): Optional: a URL on the observatory this feedback references (e.g., "https://zoningsignal.com/corridors/us-27-south-lake").
- `agent_context` (string): Optional: brief description of what the agent was trying to do — the user task that led to this surface. Helps operators understand intent without seeing only the failure point.
- `category` (string, required): Bounded categorization. 'gap' = expected capability is missing. 'error' = tool returned wrong/unexpected/malformed result. 'praise' = a surface or tool that worked exceptionally well. 'suggestion' =…
- `message` (string, required): The feedback prose itself. Be specific. What were you trying to accomplish? What was missing or wrong? Voice that survives compression. Operators read every submission.
- `suggested_resolution` (string): Optional: if you have a concrete proposal — a new tool, a renamed parameter, a missing field on a response — name it here.

Output parameters:

- `attached_invocation_count` (number): How many prior invocations (from your MCP-Session-Id) were auto-attached for operator context.
- `category` (string): The category the feedback was filed under.
- `feedback_id` (string): Stable id for the stored feedback record.
- `message` (string): Human-readable acknowledgement.
- `ok` (boolean): True when the feedback was accepted and stored.
- `received_at` (string): ISO-8601 timestamp the feedback was recorded.

### `semantic_search` (~153 tokens)

Semantic Search

Semantic search across the full corpus — every place dossier, corridor signal, meeting reading, and named-pattern brief. Returns results ranked by cosine similarity in a 1024-dimensional embedding space (Voyage AI 4 + Supabase pgvector). Use when the agent does not know the canonical entity slug or named-pattern title in advance — the search returns the readings whose semantic structure best matches the natural-language query, with type, title, similarity, and resolved URL per hit. Threshold 0.55, top 12.

Input parameters:

- `q` (string, required): The natural-language query. A phrase, an entity name, or a thematic concept all work. Asymmetric query-time embedding handles short queries cleanly. Maximum 500 characters.

Output parameters:

- `count` (number)
- `hits` (array)
- `query` (string)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/com-zoningsignal-observatory/zoningsignal#diagnostics

## Score history

- 2026-08-03: 77
- 2026-08-02: 76
- 2026-08-01: 76
- 2026-07-31: 76
- 2026-07-30: 73
- 2026-07-29: 72
- 2026-07-28: 71
- 2026-07-27: 71
- 2026-07-26: 70

## Links

- Remote endpoint: https://zoningsignal.com/mcp
- Repository: https://github.com/SuperDuperDave/zoning-signal-observatory
- Website: https://zoningsignal.com/
- Changelog RSS feed: https://verifymcp.io/servers/com-zoningsignal-observatory/zoningsignal/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/com-zoningsignal-observatory/zoningsignal/changelog.json
- HTML version of this page: https://verifymcp.io/servers/com-zoningsignal-observatory/zoningsignal