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Hei33enberg

WhiteIntel MCP Server

by Hei33enberg

find_similar

Retrieves the nearest matching dossier cards for a known entity, enabling peer discovery and clustering by similarity. Pass an entity_id to receive ranked hits with risk and similarity scores.

Instructions

Entities most similar to a given one — the nearest corpus dossier cards ('more like this'), for peer discovery and clustering around a known entity. Pass an entity_id from search_entities. Returns { id, count, hits }, each hit with entity_id, caption, kind, jurisdiction, risk and a similarity score. COVERAGE IS PARTIAL AND SKEWED — it draws on the same embedded slice as semantic_search: 990,055 of a 47,486,969 universe (2.1%), ~99.6% risk-listed and ~97% natural persons, measured 2026-08-11 from the sibling endpoint's own coverage payload. An entity outside that slice returns count: 0 with an empty hits array and HTTP 200 — that is 'not embedded', NOT 'no peers exist', and it is the common case for ordinary companies (verified: BARCLAYS BANK PLC returns zero). Never report an empty result as a finding about the entity. Fall back to semantic_search or search_entities.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoMax hits (default 10).
entity_idYesEntity uuid from search_entities.
Behavior5/5

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

With no annotations, the description carries full burden and excels: discloses partial coverage (990,055/47,486,969 = 2.1%), skew (99.6% risk-listed, 97% natural persons), behavioral nuance (HTTP 200 with count:0 for non-embedded entities), and a verified example (BARCLAYS BANK PLC). Even warns against misinterpreting empty results.

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

Conciseness5/5

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

Every sentence earns its place. The description front-loads the core purpose, then returns format, then critical limitation warnings. It is longer than average but all content is necessary behavioral caveats, and it remains well-structured and readable.

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?

Given there is no output schema, the description compensates by detailing the return shape ({ id, count, hits } with per-hit fields). It covers the tool's purpose, limitations, fallback alternatives, and edge-case behavior, making it fully self-contained for an agent.

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 coverage is 100%, so baseline is 3. Description reinforces that entity_id must come from search_entities, but adds little beyond the schema's existing descriptions for entity_id and k. It does not explain k's effect beyond schema, but no compensation needed due to high schema coverage.

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 states a clear verb+resource: 'Entities most similar to a given one — the nearest corpus dossier cards ('more like this')'. It explicitly names the intended use case (peer discovery, clustering) and distinguishes itself from semantic_search and search_entities via fallback guidance.

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?

Provides explicit when-to-use: 'for peer discovery and clustering around a known entity' and clear exclusion: coverage is partial/skewed, empty results mean 'not embedded' not 'no peers exist'. Directly names alternatives: 'Fall back to semantic_search or search_entities.'

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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