Zoning Signal

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).

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.