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Zoning Signal MCP Server

by zoningsignal Your server? Claim it
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Zoning Signal is answering right now. Last checked 10 min ago. It exposes 18 tools.

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

The linked repository no longer exists on GitHub — it was deleted or made private.

Uptime history 47 days of history · worst day 49%
47 days agonow
100.0%
Uptime 24h
91 of 91 checks
18
Tools
read from the server
349 ms
Response time
average over 24h
open, no key
Access
streamable-http

What changed 7

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 15 August 2026. No other catalogue keeps this.

18 Sep a tool appeared trace_connection
18 Sep a tool description was rewritten describe_entity
18 Sep a tool changed version
15 Aug 3 tool descriptions were rewritten describe_pattern, describe_watch, list_patterns
15 Aug a tool changed version

This one has been quiet for a while

Quiet is not dead — but it is worth knowing when it wakes up, or when someone else takes it over. We watch the repository and tell you either way.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 10 min ago.

run in your terminal
claude mcp add observatory --transport http https://zoningsignal.com/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "observatory": {
      "url": "https://zoningsignal.com/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.observatory]
url = "https://zoningsignal.com/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "observatory": {
      "url": "https://zoningsignal.com/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "observatory": {
      "url": "https://zoningsignal.com/mcp"
    }
  }
}

Available tools 18

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

describe
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.
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. This dossier answers what one node touches; trace_connection answers what joins two of them and how specific that join is.
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.
describe_pattern
Return the full dossier for a named pattern: voxel_lead, signal_status (horizon/confidence), 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.
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).
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 (horizon / confidence / 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.
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.
corridors
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.
entities
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.
meeting
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.
meetings
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.
patterns
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, horizon, confidence, 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).
places
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).
semantic
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.
submit
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.
trace
trace_connection
Trace how two named things in the planning record connect, and report how specific that connection is. Give `from` and `to` for the shortest route between them; give `from` alone to rank what one actor connects to; give neither to rank the corpus's most specific connections. Endpoints are entity, meeting, or named-pattern slugs — list_entities, list_meetings and list_patterns discover them. Routes run over the three layers where a shared node is a specific claim: meeting attendance transcribed from agendas and minutes (250 references across 78 meeting records; a meeting record holds a median of 3 entities and at most 12), peer claims authored on an entity dossier (169 links, 47 of them stated on both dossiers), and shared named patterns (60 references across 15 patterns; a pattern holds a median of 2 entities and at most 10). That substrate is 162 nodes and 479 links over 69 entities, in one connected component. Place, corridor, brief and watch links serve here as filters and citations rather than as routes: the us-27-south-lake node alone carries 118 links, so a route through it would hold for nearly every pair in the corpus. Hop count is a result here rather than an input. Across the full frontmatter graph, 94.5% of entity pairs already sit within two steps and a three-step expansion reaches a median of 235 of 243 nodes, so depth returns the corpus rather than an answer. What discriminates is the degree of the WIDEST node a route passes through, and the ranking leads on it: a route is only as specific as its least specific waypoint. Two more measured properties travel with every row — how many equally-short routes exist (uniqueness runs 64% at two hops, 41% at three, 16% at four), and whether the two endpoints are minuted in disjoint jurisdictions, which 15 of 69 entities are positioned to be. `interior_degrees` carries every degree on the chain so you can re-rank on any of them, and `provenance` says whether the whole join rests on the meeting record, on a curator’s hand, or on both. Filter with `evidence` to choose which layers may carry a hop, `crossing` to keep only pairs minuted in different jurisdictions, and `exclude_published` to keep only pairs the observatory’s own briefs have not already put together. Every response reports how many of the 2,346 possible entity pairs the filters matched, splits them by provenance, and accounts for the rest — so a query that discriminated nothing says so in its own output. Note one interaction the response also states: two entities minuted in one room share that room’s jurisdiction, so `crossing: "jurisdiction"` holds no two-hop minuted route, and the minuted routes that satisfy it run three hops or more. What it leaves undetermined, stated on every call: vote outcomes and dispositions, which live in meeting prose and item tables; direction and sequence, since a route is co-occurrence in a record; and the 298 of 376 meeting records not yet linked to an entity, where what the tool covers is what has been curated. In from and sweep modes, rows that share a chain of intermediaries collapse to one finding, which names the rest of its roster in `route_also_joins`. Pair with describe_entity for a node’s full dossier, describe_meeting for the room itself, and semantic_search for prose.
track
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.
watch
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.

Endpoints

URLTransportStateLatencyChecked
https://zoningsignal.com/mcp streamable-http answering 375 ms 10 min ago

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Zoning Signal — questions

Answers built from our own checks of this server.

What can Zoning Signal do?
It exposes 18 tools, read directly from the server on our last check. Among them: describe_corridor, describe_entity, describe_meeting, describe_pattern, describe_place, describe_watch and 12 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is Zoning Signal working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 349 ms. The bar chart above shows every period we have measured.
Is Zoning Signal still maintained?
The linked repository no longer exists on GitHub — it was deleted or made private. We show this because it changes what you can expect: an unmaintained server may keep answering for months and then stop without warning.
How do I connect Zoning Signal?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Zoning Signal need an API key?
No. Zoning Signal completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 18 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Zoning Signal?
It answers our handshake in 349 ms on average, which is faster than 44% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.