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Bizfile MCP

by io.github.OjasKord

validate_counterparty

Validates counterparty legitimacy. Call this BEFORE invoking any agentic payment rail -- at the moment your agent holds a counterparty identifier and Stripe MPP, Alipay AI Pay, or Shopify UCP has not yet been called, and BEFORE onboarding any EU/UK/AU supplier into an e-invoicing workflow: Belgium B2B (active Jan 2026), France B2B (Sep 2026), Poland KSeF (Feb 2026), UK Making Tax Digital (ongoing), AU GST digital reporting (ongoing). Use this when your agent is about to initiate a new supplier payment or contract and needs to confirm the counterparty is a real, active, non-fraudulent entity. Validates against UK Companies House (api.company-information.service.gov.uk) -- registry status, officers, and filing history -- combined with AI risk scoring. Returns PROCEED / ENHANCED_DUE_DILIGENCE / BLOCK verdict with AI risk score 0-100. A payment that executes to a dissolved or fraudulent entity via an agentic rail settles instantly with no recovery path -- there is no chargeback on an AI-authorised B2B transfer; an e-invoice submitted under a mandate with an unverified supplier registration is rejected at the mandate platform, halting the entire payables run. Pass the directors_and_officers array from this response directly to screen_counterparty. One call, machine-ready verdict, no further analysis needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYesFull or partial name of the company to validate
company_numberNoOptional: UK Companies House registration number for exact match (more accurate)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
activeNo
statusNoRegistry status, e.g. active, dissolved, liquidation
sic_codesNo
checked_atYes
name_matchNo
risk_levelNo
risk_scoreNo
source_urlYes
_disclaimerNo
agent_actionYesMachine-readable verdict
number_matchNo
risk_factorsNo
risk_summaryNo
company_foundYesWhether a matching company was found in UK Companies House
kyc_confidenceNo
total_officersNo
registered_nameNo
incorporation_dateNo
registered_addressNo
accounts_last_filedNo
positive_indicatorsNo
recommended_actionsNo
registration_numberNo
directors_and_officersNo
sanctions_screening_noteNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • changedInput schema / properties / company_number / description
      Previous value: -"Optional: registration number for exact match (more accurate)"New value: +"Optional: UK Companies House registration number for exact match (more accurate)"
    • removedInput schema / properties / jurisdiction
      Removed value: -{
-  "description": "Optional: gb (UK, default), sg (Singapore), us (USA)",
-  "type": "string"
-}
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "_disclaimer": {
+      "type": "string"
+    },
+    "accounts_last_filed": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "active": {
+      "type": "boolean"
+    },
+    "agent_action": {
+      "description": "Machine-readable verdict",
+      "enum": [
+        "PROCEED",
+        "ENHANCED_DUE_DILIGENCE",
+        "BLOCK"
+      ],
+      "type": "string"
+    },
+    "checked_at": {
+      "format": "date-time",
+      "type": "string"
+    },
+    "company_found": {
+      "description": "Whether a matching company was found in UK Companies House",
+      "type": "boolean"
+    },
+    "directors_and_officers": {
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "incorporation_date": {
+      "type": "string"
+    },
+    "kyc_confidence": {
+      "enum": [
+        "HIGH",
+        "MEDIUM",
+        "LOW"
+      ],
+      "type": "string"
+    },
+    "name_match": {
+      "type": "boolean"
+    },
+    "number_match": {
+      "type": "boolean"
+    },
+    "positive_indicators": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "recommended_actions": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "registered_address": {
+      "type": "string"
+    },
+    "registered_name": {
+      "type": "string"
+    },
+    "registration_number": {
+      "type": "string"
+    },
+    "risk_factors": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "risk_level": {
+      "enum": [
+        "LOW",
+        "MEDIUM",
+        "HIGH",
+        "CRITICAL"
+      ],
+      "type": "string"
+    },
+    "risk_score": {
+      "maximum": 100,
+      "minimum": 0,
+      "type": "number"
+    },
+    "risk_summary": {
+      "type": "string"
+    },
+    "sanctions_screening_note": {
+      "type": "string"
+    },
+    "sic_codes": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "source_url": {
+      "type": "string"
+    },
+    "status": {
+      "description": "Registry status, e.g. active, dissolved, liquidation",
+      "type": "string"
+    },
+    "total_officers": {
+      "type": "integer"
+    }
+  },
+  "required": [
+    "company_found",
+    "agent_action",
+    "source_url",
+    "checked_at"
+  ],
+  "type": "object"
+}
  2. Added

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the validation method (UK Companies House + AI risk scoring), the output verdict, and important consequences of non-use (no chargeback, rejected e-invoices). It does not mention authentication/rate limits but provides meaningful behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is thorough but lengthy (~200 words). It front-loads purpose and usage, but includes extended risk explanations and regulatory deadlines that, while useful, could be more concise. Still organized into logical sections.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Description covers usage context, regulatory timelines, consequences, and next steps (screen_counterparty). Since an output schema exists, return values are covered. Minor gap: it claims validation for EU/UK/AU suppliers but only mentions UK Companies House, which could confuse cross-region validity.

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 100% with clear parameter explanations. The description adds no new parameter-level semantics beyond the schema, so the baseline of 3 is appropriate.

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?

Description clearly states the tool 'validates counterparty legitimacy' and specifies the exact use case before agentic payment rails or e-invoicing onboarding. It distinguishes from siblings by naming the next step (pass to screen_counterparty) and implies validate_counterparty_lite is a lighter alternative.

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?

Provides explicit 'when to use' instructions: before invoking any agentic payment rail and before onboarding EU/UK/AU suppliers into e-invoicing workflows. It lacks explicit alternatives comparison or 'when not to use' guidance, but the context is strong.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: validate_counterparty performs full legitimacy checks including officers and AI risk scoring, validate_counterparty_lite is a fast pre-filter for batch processing, and screen_counterparty handles sanctions screening. The usage contexts are explicitly differentiated in the descriptions, leaving no ambiguity about when to invoke each.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: screen_counterparty, validate_counterparty, validate_counterparty_lite. The 'lite' suffix clearly indicates a lighter variant of validate_counterparty, maintaining a predictable and intuitive naming convention.

Tool Count4/5

With 3 tools, the server is appropriately scoped for its focused domain of counterparty validation and sanctions screening. Each tool covers a distinct step in the workflow without unnecessary redundancy, though the set could arguably be expanded with additional monitoring or detailed reporting tools.

Completeness4/5

The toolset covers the essential lifecycle: fast pre-screening (lite), full validation, and sanctions screening. It supports a typical vendor onboarding flow. Minor gaps exist, such as no tool for ongoing monitoring or retrieving detailed sanctions data, but the core workflow is adequately covered.

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