list_firm_kindsBreakdown of the published firm directory by organisation kind, with a live row count per kind. Use this before search_firms to know what the population actually is instead of guessing.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | ||
| tool | Yes | ||
| _meta | No | ||
| _attribution | Yes | ||
| result_count | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds useful behavioral detail: the counts are 'live', indicating freshness and that the data reflects the current state, which is not conveyed by annotations or schema.
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 sentences, front-loaded with the core function and then a practical usage hint. Every word earns its place, with no redundancy 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?
Given the simple parameterless signature, rich annotations, and presence of an output schema, the description provides sufficient context for an agent to understand the tool's role and when to invoke it. It adds domain context (published directory, live counts) and usage guidance, making it complete for this tool's complexity.
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?
There are no parameters, so the description carries no parameter burden. The baseline for zero parameters is 4, and though the description doesn't add parameter-specific meaning, it explains the output grouping by organization kind, which helps set expectations for the result.
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 clearly states the tool provides a breakdown of the published firm directory by organization kind, with live counts. It distinguishes itself from sibling tools like search_firms by focusing on population-level aggregation rather than individual firm lookup, and explicitly frames it as a precursor to searching.
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 explicitly says to use this tool before search_firms, providing clear context and a naming the alternative. It lacks explicit 'when not to use' exclusions, but the guidance to use it as a preliminary step is direct and helpful.
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.
Each tool targets a distinct resource or action (firms, clients, modules, concepts, studies, opportunities, rates, news, knowledge graph). Some pairs like find_academy_modules vs list_sap_modules and find_sap_clients vs search_firms could be confused, but the descriptions explicitly disambiguate them.
All tool names follow a consistent verb_noun pattern in lowercase snake_case: find_, get_, list_, search_, count_, query_. Verbs are consistently used for their roles (find/search for querying, get for single items, list for enumerations), with no mixed casing or style.
20 tools is on the higher end, but the server covers a broad domain with multiple distinct datasets (directory, clients, academy, concepts, studies, opportunities, rates, news, graph). Each tool earns its place, though the count is slightly above the ideal 3-15 range.
The domain is a read-only intelligence platform, and it provides search/list and get operations for every major entity: firms, clients, modules, concepts, studies, and opportunities. The knowledge graph adds relational querying, and rates/news are covered. No essential lifecycle operations are missing for the stated purpose.