zoning-signal

endpoint

https://zoningsignal.com/mcp

protocol

streamable-http ·2025-11-25

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is zoning-signal live?

Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.

Is zoning-signal free to use?

Yes — the hub reached it with no key and no payment.

What tools does zoning-signal have?

18 tools: describe_corridor, submit_agent_feedback, describe_pattern, describe_place, describe_watch, describe_zoning_signal, describe_meeting, describe_entity, ….

Is zoning-signal safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• list_entities reads open 15h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "place": {
        "type": "string",
        "description": "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.jurisdiction includes the place slug."
      },
      "search": {
        "type": "string",
        "description": "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-based code\")."
      },
      "entity_class": {
        "enum": [
          "Person",
          "Organization",
          "Legislation",
          "CreativeWork"
        ],
        "type": "string",
        "description": "Filter by entity class. \"Person\" = board members, attorneys, applicants (individuals), elected officials. \"Organization\" = developer firms, law firms, agencies, HOAs, planning consultancies. \"Legislation\" = state statutes, city ordinances, code sections, design standards. \"CreativeWork\" = specific projects, case numbers, master plans, infrastructure programs. Omit to return all classes."
      }
    },
    "additionalProperties": false
  }

  arguments 24 lines copy
• list_meetings reads open 4h ago

  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.

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  {
    "type": "object",
    "properties": {
      "to_date": {
        "type": "string",
        "format": "date",
        "description": "Inclusive upper bound (ISO 8601 date). Omit for the latest reading."
      },
      "from_date": {
        "type": "string",
        "format": "date",
        "description": "Inclusive lower bound (ISO 8601 date). Omit to span back to the earliest reading."
      },
      "jurisdiction": {
        "type": "string",
        "description": "Optional case-insensitive substring to filter by city (e.g., \"minneola\"). Omit for all cities."
      }
    },
    "additionalProperties": false
  }

  arguments 20 lines copy
• list_places reads open 4h ago

  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 it 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).

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  {
    "type": "object",
    "properties": {
      "state": {
        "type": "string",
        "description": "Optional US state name (e.g., \"Florida\") to filter the result set. Omit for all places across all states."
      }
    },
    "additionalProperties": false
  }

  arguments 10 lines copy
• get_track_record reads open 19h ago

  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. When multiple scopes are supplied, jurisdiction takes precedence over corridor, then brief. Use when an agent or user wants to assess Zoning Signal's historical forecasting accuracy before citing a current prediction. Misreads are reported.

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  {
    "type": "object",
    "properties": {
      "brief": {
        "type": "string",
        "description": "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": {
        "type": "string",
        "description": "Optional: scope to a corridor slug (e.g., \"us-27-south-lake\"). Returns the aggregate track record across the corridor's constituent places."
      },
      "jurisdiction": {
        "type": "string",
        "description": "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."
      }
    },
    "additionalProperties": false
  }

  arguments 18 lines copy
• list_patterns reads open 4h ago

  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 it from current_named_patterns to align with the canonical content-type vocabulary (loader: getAllContent("pattern"); URLs: /patterns/{slug}; describe tool: describe_pattern).

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  {
    "type": "object",
    "properties": {},
    "additionalProperties": false
  }

  arguments 5 lines copy
• list_corridors reads open 15h ago

  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.

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  {
    "type": "object",
    "properties": {},
    "additionalProperties": false
  }

  arguments 5 lines copy
• list_watch_items reads open 19h ago

  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.

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

  arguments 37 lines copy
• trace_connection reads open 19h ago

  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 (249 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 (162 links, 44 of them stated on both dossiers), and shared named patterns (58 references across 15 patterns; a pattern holds a median of 2 entities and at most 10). That substrate is 161 nodes and 469 links over 68 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 117 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 65% at two hops, 40% at three, 14% at four), and whether the two endpoints are minuted in disjoint jurisdictions, which 15 of 68 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 2,278 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 347 of 425 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.

  mcp-tool

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  {
    "type": "object",
    "required": [],
    "properties": {
      "to": {
        "type": "string",
        "description": "The other endpoint, drawn from the same three vocabularies as `from`. Supplying `to` without `from` is accepted and read as `from`, because the graph is undirected; the response echoes that as query.normalized_as."
      },
      "from": {
        "type": "string",
        "description": "One endpoint slug, resolved in order against entities, meetings, then named patterns: the three vocabularies in which a shared node is a specific claim. No slug is shared between them on the deployed corpus, so a bare slug resolves unambiguously and needs no type prefix (e.g., \"tara-tedrow\", \"minneola-pz-2025-01\", \"self-storage-canary\"). Omit both endpoints to rank the whole corpus."
      },
      "limit": {
        "type": "integer",
        "default": 5,
        "maximum": 25,
        "minimum": 1,
        "description": "How many connections to return. It caps connections; routes within one connection are shown five at a time in pair mode and one at a time in from and sweep modes, and `routes_shown` beside the exact `shortest_path_count` names how many. A value outside 1–25 is refused rather than clamped, so selectivity.returned always agrees with what you asked for."
      },
      "crossing": {
        "enum": [
          "any",
          "jurisdiction"
        ],
        "type": "string",
        "default": "any",
        "description": "\"any\" ranks every matching pair. \"jurisdiction\" keeps only pairs whose record-backed jurisdiction footprints are disjoint and both present, computed from the jurisdiction on each minuted meeting rather than from an authored place list."
      },
      "evidence": {
        "type": "array",
        "items": {
          "enum": [
            "minuted",
            "authored",
            "named_pattern"
          ],
          "type": "string"
        },
        "default": [
          "minuted",
          "authored",
          "named_pattern"
        ],
        "maxItems": 3,
        "minItems": 1,
        "description": "Which layers may carry a hop. \"minuted\" = attendance transcribed from an agenda or minutes; \"authored\" = a peer claim stated on an entity dossier; \"named_pattern\" = two entities listed as exhibits of one named pattern. This is the substrate choice and the largest single lever on the answer: a restricted set fragments the graph (minuted alone is 5 components), and the response reports substrate.components and a fragmentation note rather than returning a bare no_path.",
        "uniqueItems": true
      },
      "exclude_published": {
        "type": "boolean",
        "default": false,
        "description": "true keeps only the pairs the observatory's own briefs have yet to put together, tested against the union of an entity's appears_in_briefs and the briefs that name it."
      }
    },
    "additionalProperties": false
  }

  arguments 56 lines copy
• describe_corridor reads unknown 1h ago

  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.

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  {
    "type": "object",
    "required": [
      "slug"
    ],
    "properties": {
      "lens": {
        "enum": [
          "synthesis",
          "developer",
          "investor",
          "broker",
          "attorney",
          "business",
          "resident",
          "civic-leader"
        ],
        "type": "string",
        "default": "synthesis",
        "description": "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/regulator → attorney; realtor/intermediary → broker; civic/government → civic-leader; homeowner → resident; operator/site-selector → business; builder → developer."
      },
      "slug": {
        "type": "string",
        "description": "The corridor slug (e.g., \"us-27-south-lake\"). Use list_corridors to discover available slugs."
      }
    },
    "additionalProperties": false
  }

  arguments 28 lines copy
• submit_agent_feedback changes data unknown never probed

  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, so there is 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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "category",
      "message"
    ],
    "properties": {
      "message": {
        "type": "string",
        "maxLength": 8192,
        "description": "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."
      },
      "category": {
        "enum": [
          "gap",
          "error",
          "praise",
          "suggestion",
          "citation_request"
        ],
        "type": "string",
        "description": "Bounded categorization. 'gap' = expected capability is missing. 'error' = tool returned wrong/unexpected/malformed result. 'praise' = a surface or tool that worked exceptionally well. 'suggestion' = a refinement (better tool description, additional argument, alternative output shape). 'citation_request' = a claim or fact you want surfaced with a stable citation @id."
      },
      "about_url": {
        "type": "string",
        "description": "Optional: a URL on the observatory this feedback references (e.g., \"https://zoningsignal.com/corridors/us-27-south-lake\")."
      },
      "about_tool": {
        "type": "string",
        "description": "Optional: the tool name this feedback is about (e.g., \"describe_corridor\"). Lets operators rollup feedback per tool."
      },
      "agent_context": {
        "type": "string",
        "maxLength": 4096,
        "description": "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."
      },
      "suggested_resolution": {
        "type": "string",
        "description": "Optional: if you have a concrete proposal (a new tool, a renamed parameter, a missing field on a response), name it here."
      }
    },
    "additionalProperties": false
  }

  arguments 43 lines copy
• describe_pattern reads unknown 12h ago

  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.

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  {
    "type": "object",
    "required": [
      "slug"
    ],
    "properties": {
      "slug": {
        "type": "string",
        "description": "The pattern slug (e.g., \"self-storage-canary\"). Use current_named_patterns to discover available slugs."
      }
    },
    "additionalProperties": false
  }

  arguments 13 lines copy
• describe_place reads unknown 12h ago

  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. This is 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).

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  {
    "type": "object",
    "required": [
      "slug"
    ],
    "properties": {
      "lens": {
        "enum": [
          "synthesis",
          "developer",
          "investor",
          "broker",
          "attorney",
          "business",
          "resident",
          "civic-leader"
        ],
        "type": "string",
        "default": "synthesis",
        "description": "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/land-use-counsel/regulator → attorney; realtor/intermediary/real-estate-broker → broker; civic/government/official/governance → civic-leader; homeowner/citizen → resident; operator/site-selector/occupier → business; builder/land-developer → developer."
      },
      "slug": {
        "type": "string",
        "description": "The place slug (e.g., \"clermont-florida\"). Use list_places to discover available slugs."
      }
    },
    "additionalProperties": false
  }

  arguments 28 lines copy
• describe_watch reads unknown 12h ago

  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.

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  {
    "type": "object",
    "required": [
      "slug"
    ],
    "properties": {
      "slug": {
        "type": "string",
        "description": "The watch slug (e.g., \"lake-bright-council-mar-23\"). Use list_watch_items to discover available slugs."
      }
    },
    "additionalProperties": false
  }

  arguments 13 lines copy
• describe_zoning_signal reads unknown never probed

  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.

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  {
    "type": "object",
    "properties": {},
    "additionalProperties": false
  }

  arguments 5 lines copy
• describe_meeting reads unknown 15h ago

  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, namely developer, investor, broker, attorney, business, resident and 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.

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  {
    "type": "object",
    "required": [
      "slug"
    ],
    "properties": {
      "lens": {
        "enum": [
          "synthesis",
          "developer",
          "investor",
          "broker",
          "attorney",
          "business",
          "resident",
          "civic-leader"
        ],
        "type": "string",
        "description": "Optional cognitive lens. Default: synthesis (the whole-picture multi-projection view). Canonical lenses: developer, investor, broker, attorney, business, resident, civic-leader. Aliases route to canonical (legal/lawyer/counsel/regulator → attorney; realtor/intermediary → broker; civic/government → civic-leader; homeowner → resident; operator/site-selector → business; builder → developer). When the requested lens is not present in the dossier body, the response falls back to synthesis with fell_back_to_synthesis: true."
      },
      "slug": {
        "type": "string",
        "description": "The meeting slug (e.g., \"leesburg-pc-2026-01\"). Use list_meetings or meeting_index to discover available slugs."
      }
    },
    "additionalProperties": false
  }

  arguments 27 lines copy
• describe_entity reads unknown 1h ago

  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.

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  {
    "type": "object",
    "required": [
      "slug"
    ],
    "properties": {
      "slug": {
        "type": "string",
        "description": "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."
      }
    },
    "additionalProperties": false
  }

  arguments 13 lines copy
• semantic_search reads unknown 1h ago

  Search every record by exact words and by meaning at once: meeting agendas and minutes, place dossiers, corridors, named patterns and briefs, entities and watch items. Finds a case, ordinance or resolution number, a parcel id or alternate key, an address, a project, firm or person, or a plain-language question. Ranking fuses three lists by reciprocal rank: full-text match (every word as written; "quotes", OR and -word work as in web search), title match, and voyage-4 embedding similarity. Each hit carries its true cosine `similarity` and `matched` (title, text, meaning). Every response states `scope` (the query names a covered place, a place outside coverage, or no place) and `evidence` (strong when a top record contains the query's terms or closely resembles it); when either needs explaining it adds a `note` and the coverage. `weak_match` is deprecated and will be removed in 2.4.0: it is true when scope is outside or evidence is not strong, so read those instead. Coverage: 25 jurisdictions in Lake, Orange, Osceola, Polk and Seminole counties, Florida, from 2024, listed in the coverage resource. Top 12.

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  {
    "type": "object",
    "required": [
      "q"
    ],
    "properties": {
      "q": {
        "type": "string",
        "description": "A name, a number or a question. Text matching needs every word to appear in a record, so extra words narrow it; meaning matching tolerates any wording. Maximum 500 characters."
      }
    },
    "additionalProperties": false
  }

  arguments 13 lines copy
• meeting_index reads unknown 8h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "city"
    ],
    "properties": {
      "city": {
        "type": "string",
        "description": "City name (e.g., \"Clermont\"). Case-insensitive."
      },
      "to_date": {
        "type": "string",
        "format": "date",
        "description": "Inclusive upper bound (ISO 8601 date). Omit for the latest reading."
      },
      "from_date": {
        "type": "string",
        "format": "date",
        "description": "Inclusive lower bound (ISO 8601 date). Omit to span back to the earliest reading."
      }
    },
    "additionalProperties": false
  }

  arguments 23 lines copy

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