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Chercher des entreprises

basile_search_companies
Read-onlyIdempotent

Cherche des ENTREPRISES (registre légal + LinkedIn + Google My Business) dans Basile. Renvoie { total, establishmentsTotal, leads[], pagination.nextToken }. limit défaut 25, max 1000. total = sociétés (dédupliquées par domaine) ; establishmentsTotal = POINTS DE VENTE (fiches Google). Pour exporter les points de vente : basile_export avec mode:"locations". ⚠️ COÛT : 1 crédit par fiche renvoyée (limit=25 → 25 crédits), à chaque appel. Compte d'abord avec basile_count (gratuit et exact) ; le total renvoyé ici est plafonné à 100 000.

FORME DES FILTRES :

  • Filtre texte = {"include":[...], "exclude":[...]} — include = OR, exclude = NOT.

  • Plusieurs filtres différents = ET entre eux.

  • People : filtres numériques = range objet {">=":n,"<=":n} (ops >, >=, <, <=).

  • Companies : filtres numériques = champs simples _min / _max (ex. capital_min, headcount_max).

FILTRES COMPANIES (POST /companies/find) : Généraux (3 sources Legal+LKI+GMB) : name (contains), activity (métier unifié — le plus puissant ; IDs via basile_activity_suggest), with_email (bool, uniquement les entreprises dont le domaine a au moins un email crawlé). Legal-only : naf_code (préfixe ".x" possible, ex "41.x"), headquarters_naf_code, headquarters_department_code (ex "75","13"), headquarters_postal_code, legal_form (accepte code court SARL/SAS/SASU/EURL/SA/SCI…, code INSEE 5710, OU libellé complet), legal_category (code INSEE), siren, capital_min/capital_max, publishable (bool), creation_date_min/creation_date_max (année ex "2015"), created_since_months (nombre — entreprises créées dans les N derniers mois, ex 3 ; plus précis que creation_date pour « créées récemment »). Région : utiliser region (NOM canonique, ex "Île-de-France") ou headquarters_department_code (ex "75"). ⚠️ headquarters_region_code (code INSEE) est IGNORÉ (no-op, pas de résolveur) — ne pas l'utiliser. Legal+LKI (exclut GMB) : headquarters_city, headquarters_country_code (ex "FR"), headcount_min/headcount_max (effectif). LinkedIn-only : domain (contains), tech_stack (techno du site, ex "Shopify"/"WordPress"). ⚠️ followers : pas de données → ne pas utiliser. ⚠️ industry_main / company_type = LinkedIn, lacunaires/peu fiables → pour un secteur préférer activity ou naf_code. GMB-only : rating_min/rating_max (0-5), reviews_min/reviews_max, is_opening (bool, ouvert dimanche). Spécial : company_ceased (bool) — METTRE false par défaut (entreprises actives uniquement). Pilotage source : source ("Legal"|"LKI"|"GMB"), with_legal_data (bool), with_linkedin_page (bool). CONSEILS : métier → activity (IDs via basile_activity_suggest) sinon naf_code. France → headquarters_country_code:{include:["FR"]} ou headquarters_department_code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNb de résultats à renvoyer (défaut 25).
filtersYesFiltres companies (voir description).
paginationTokenNoCurseur `pagination.nextToken` de la page précédente.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNb de fiches réellement renvoyées dans cette page (= crédits débités).
leadsYesLes entreprises de cette page.
totalYesSociétés dédupliquées, total PLAFONNÉ à 100 000 — pour l'exact, basile_count.
nextTokenYesCurseur de la page suivante ; null quand il n'y en a plus.
establishmentsTotalYesPoints de vente (fiches Google) derrière ces sociétés ; null si Google ne participe pas.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

The annotations already convey read-only and idempotent behavior, so the description earns credit for going beyond them. It discloses the 1-credit-per-record cost, the 100,000 cap on `total`, the `kredentials` no-op behavior of headquarters_region_code, and the semantic difference between companies and points de vente. This is strong operational context not present in the annotations.

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 description is long, but it is well-structued with headers, source categories, and front-loaded critical information such as output shape, cost, and pagination. Every large block maps to a real filter/selection decision, though the density of the filter list makes it require careful reading.

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 complex, filter-heavy tool with a rich output schema, the description provides everything needed to call it correctly: return semantics, limit behavior, pagination token, cost, source coverage, no-op fields, and recommended tools. I see no decision-relevant gap that the agent would be forced to guess about.

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

Parameters5/5

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

The input schema treats `filters` as an opaque object with no structured properties, so the description carries nearly all the filter semantics. It defines the include/exclude shape, AND/OR behavior, numeric operator formats, legal-related fields, source-specific fields, special flags like company_ceased, and avoided fields. This is far beyond the minimal schema descriptions.

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?

The description explicitly states that the tool searches companies (ENTREPRISES) across Legal, LinkedIn, and Google My Business, names the exact return shape (`total`, `establishmentsTotal`, `leads[]`, `pagination`), and distinguishes itself from nearby tools like basile_search_people, basile_export, and basile_count. An agent can identify when to use this tool without opening schemas.

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

Usage Guidelines5/5

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

The description gives concrete usage guidance: use basile_count first for an exact free count, use basile_export with mode:'locations' to export points de vente, and prefer activity or naf_code over unreliable LinkedIn fields. It also warns about unused/no-op fields, so when-not-to-use guidance is explicit.

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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