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BORME MCP Server

by bormeapi
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Python
Remote

Recent risk signals

recent_signals
Read-onlyIdempotent

Retrieve a sample of up to 10 risk signals from BORME and public sources dated within the last 7 days, optionally filtered by signal type, to identify risk events reported this week.

Instructions

Get a sample of the latest risk signals derived from BORME and public sources: at most 10 rows dated within the last 7 days.

Use it for "what risk signals appeared this week"; not a per-company check (use company_history) and not the full feed (the hosted API's /v1/signals).

Returns JSON {count, window, sample_cap, signals: [{signal_type, date, company_slug, company_name, province, details}], full_feed}. AEAT_DEBTOR comes from the AEAT's yearly debtors list (art. 95 bis LGT): details carry listed_on, list_year, is_current_list (true for 12 months from listed_on) and debt_eur, the published total at 31 December of the prior year. Say the company was on the list published on that date; never say the debt is still outstanding. count 0 means none in the window, not that a company is risk-free; an unknown signal_type is refused with the valid list.

Read-only; one request per call. Needs the risk plan: send your API key in the X-API-Key header (https://bormeapi.com/account).

Input Schema

NameRequiredDescriptionDefault
signal_typeNoOptional, one of INSOLVENCY_FILED, ACCORDION, MASS_EXIT, OWNERSHIP_CHANGE, DISTRESS_COMBO, PHOENIX, ADDRESS_CLUSTER, AEAT_DEBTOR, PUBLIC_CONTRACT_AWARD, e.g. MASS_EXIT; omit for all types.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.1.1
    • changedInput schema / properties / signal_type / description
      Previous value: -"One signal type to filter by (see the tool description); omit for all types."New value: +"Optional, one of INSOLVENCY_FILED, ACCORDION, MASS_EXIT, OWNERSHIP_CHANGE, DISTRESS_COMBO, PHOENIX, ADDRESS_CLUSTER, AEAT_DEBTOR, PUBLIC_CONTRACT_AWARD, e.g. MASS_EXIT; omit for all types."
  2. Changed5 schema fields changedv1.1.1
    • removedInput schema / properties / signal_type / anyOf
      Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
    • removedInput schema / properties / signal_type / default
      Removed value: -null
    • addedInput schema / properties / signal_type / description
      Added value: +"One signal type to filter by (see the tool description); omit for all types."
    • addedInput schema / properties / signal_type / type
      Added value: +"string"
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "recent_signalsOutput",
-  "type": "object"
-}New value: +null
  3. Addedv1.1.0

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, it discloses the sample cap, the 7-day window, the auth requirement (X-API-Key, risk plan), one-request-per-call, the exact return shape, and subtle semantics: count 0 does not mean risk-free, and an unknown signal_type is refused with the valid list. The AEAT_DEBTOR note adds an important interpretive constraint (report the historical listing, never say the debt is outstanding).

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?

It is front-loaded with purpose and scope before the alternatives and caveats, and every paragraph (return shape, AEAT handling, count-0 semantics, auth) carries information the agent needs. It runs long, but there is little true waste; a slightly tighter AEAT paragraph would be the only trim.

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?

With no output schema, the description takes responsibility by listing the JSON return fields and explaining count/window/sample_cap/full_feed. Combined with the auth note and the AEAT interpretation caveat, an agent has everything needed to call and report results correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the enum values are already documented in the schema and the baseline would be 3. The description adds value beyond it by explaining rejection behavior for an unknown signal_type, which the schema does not state, plus the omit-for-all-types default implied by the usage guidance.

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 opens with a specific verb and resource ("Get a sample of the latest risk signals") and bounds it precisely: at most 10 rows within the last 7 days. It also names the sibling it is not (company_history) and distinguishes itself from the full hosted feed, so an agent can tell it apart from search_events and daily_digest without opening a schema.

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?

It gives an explicit use case ("what risk signals appeared this week"), two explicit exclusions with the alternative named (per-company check -> company_history; full feed -> /v1/signals), and the plan/auth prerequisite. Virtually nothing about when to select this tool is left to inference.

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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