Search every node in the corpus — companies AND people — by name. Returns entity ids for get_dossier / trace_ownership_path.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | ||
| risk | No | ||
| type | No | ||
| juris | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful extra context by stating that the search covers all node types and that the output is intended for downstream dossier/ownership tools. It does not contradict annotations. Details about ranking, match behavior, or pagination are absent, but the added purpose context goes beyond annotation defaults.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, with the main action front-loaded. Every clause earns its place: the scope and the return-value purpose are both conveyed without redundant language or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description captures the core purpose and return-value use case, which is enough for a simple search tool. However, with 5 parameters (including 2 enums), no output schema, and 0% schema description coverage, there are meaningful gaps about filtering, result limits, and formatting that an agent would need to invoke the tool correctly in edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only mentions 'by name', which maps loosely to the q parameter. The other five parameters (risk, type, juris, limit) are completely unexplained in both the schema and description. For a tool with this many parameters and no schema descriptions, the description fails to compensate for the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Search') plus a precise resource scope ('every node in the corpus — companies AND people — by name'), clearly distinguishing it from sibling search_companies and lookup tools. It also explains the intended downstream use ('Returns entity ids for get_dossier / trace_ownership_path'), reinforcing purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies clear usage context: use this tool to find entity IDs for get_dossier/trace_ownership_path and to search across both companies and people. It does not explicitly list exclusions or compare with alternatives like search_companies or semantic_search, but the scope statement provides an implied when-to-use boundary that is useful to an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes (search, lookup, get details, graph traversal, coverage maps, payment). Some overlap exists between lookup_company and lookup_by_identifier (both resolve identifiers, though one is UK-specific), and between get_entity vs get_company_details, but descriptions clarify distinctions. find_similar and semantic_search both find similar entities but different mechanisms.
Uses a mix of verb_noun and noun_phrases: buy_dossier, claim_dossier, check_offshore_exposure, find_similar, get_pricing, get_pulse, graph_neighbourhood, list_asset_coverage, lookup_by_identifier, search_entities, trace_ownership_path. While most use underscores and verbs like get/lookup/search/list, there are inconsistencies like 'graph_neighbourhood' (noun start) vs 'trace_ownership_path' (verb start). Overall readable but not perfectly consistent.
23 tools is at the high end but justifiable for a comprehensive intelligence server covering search, lookup, graph, coverage, payments, and entity details. 'buy_dossier' is considered one of the tools and adds bulk but not excessive. It is appropriate for the scope.
Covers a wide range: search (search_entities, search_companies, semantic_search), identifier lookup (lookup_by_identifier, lookup_company), entity details (get_entity, get_company_details, get_financials), graph (graph_neighbourhood, graph_path, trace_ownership_path), coverage maps (list_jurisdictions, list_asset_coverage), sanctions (get_sanctions, check_offshore_exposure), plus payment flow (get_pricing, buy_dossier, claim_dossier, get_payment_link). Missing perhaps a 'get_relationships' separate tool but get_entity includes direct relationships. Also no explicit 'list_entities' but search covers it. Minor gaps like no bulk operations besides resolve.
