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by ai.tulina.mcp
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urba_epfif

EPFIF land-control intervention status of a commune (Île-de-France only).

Returns whether the commune is under an EPFIF sector (veille / maîtrise / ORCOD-IN) — a land-pressure signal the GPU zoning does not carry (pre-emption often delegated to the EPFIF). secteur_epfif is False outside any known sector, null if the source is unavailable. Data scraped live from the EPFIF cartography page and cached. Takes the INSEE commune code.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "code_insee": "<code_insee>"
    },
    "name": "urba_epfif"
  }
}

Input Schema

NameRequiredDescriptionDefault
_orgNoOrg (id) de CET APPEL. `oto_list_orgs()`.
_groupNoÉquipe (id) de CET APPEL, sous son org parente.
_run_idNoOBLIGATOIRE à chaque appel dès `run_start` ou `data_claim_next` : sans lui, l'écriture sur une ligne réservée est refusée. Hérité seulement si le serveur tient un run actif — n'y compte pas.
_projectNoProjet (id) de CET APPEL — jeton PRIMAIRE. `oto_project(op='list')`.
_instanceNoInstance de connecteur PRÉCISE (un credential exact). `oto_instance(op='list')`.
code_inseeYes

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full load and does well: it discloses the possible enum-like values (veille / maîtrise / ORCOD-IN), the exact return field name, that `secteur_epfif` is False outside any sector and null when the source is unavailable, and that data is scraped live and cached. It omits cache freshness/TTL and failure behavior beyond the null sentinel, which keeps it from a 5.

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 purpose and geographic bound are front-loaded in the first clause, and every following sentence adds information (return values, null/false semantics, caching). The parenthetical asides and inline code formatting make it slightly denser than necessary, but no sentence is filler.

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?

For a single-value read-only enrichment tool with no output schema, the description supplies the complete return contract: the field name, its possible values, and what False and null mean, plus the source and caching behavior. Nothing an agent needs to call this correctly or interpret the result is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 83%, so the baseline is 3; the only undocumented parameter is `code_insee`, and the description covers it by stating it takes the INSEE commune code and is limited to Île-de-France. It adds correct meaning for the single domain parameter but says nothing about the five shared _org/_group/_run_id/_project/_instance plumbing parameters, which the schema already documents.

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?

States a concrete verb and resource ("EPFIF land-control intervention status of a commune"), bounds the scope geographically ("Île-de-France only"), and explicitly contrasts itself with the sibling GPU zoning data ("a land-pressure signal the GPU zoning does not carry"). An agent can tell it apart from urba_zonage without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The geographic restriction (Île-de-France only) and the relation to URBA zonage give clear conditions for use, and the parenthetical about pre-emption being delegated to EPFIF explains why an agent would reach for this tool. It stops short of an explicit routing statement (when to skip this and use a named alternative), so it is strong but not a full when/when-not contract.

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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