Retrieve Bulgarian pension manager metadata for UPF, PPF, and VPF/DPF schemes.
Use this to discover licensed managers and their supported scheme types before running rankings or metric calculations over FSC-tracked pension data. Data freshness: reflects latest ingested official FSC manager/scheme coverage.
| Name | Required | Description | Default |
|---|---|---|---|
| scheme_code | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It adds useful context about data freshness ('reflects latest ingested official FSC manager/scheme coverage') and implies a read-only operation via 'Retrieve'. However, it does not disclose potential exclusions, error behavior, or limitations, leaving a moderate transparency gap.
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 concise and front-loaded. It consists of a one-sentence purpose, a usage tip, and a data-freshness note—no filler or redundant content. Every sentence earns its place.
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?
For a simple list tool with a single optional parameter and an output schema, the description adequately conveys purpose and usage context. However, the lack of parameter semantics explanation and the 'VPF' vs enum mismatch prevent the description from being fully complete for correct invocation. It does not explain what the output contains beyond 'metadata', but the output schema presumably covers that.
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?
The input schema has one parameter (scheme_code) with 0% description coverage, and the description never explicitly explains how the parameter filters results. It mentions scheme types in the purpose, but does not define valid values or the meaning of null/default. This is a significant gap for the only input parameter.
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 retrieves Bulgarian pension manager metadata for UPF, PPF, and VPF/DPF schemes, using a specific verb and resource. However, it mentions 'VPF/DPF' while the input schema only supports 'upf', 'ppf', and 'dpf', creating slight ambiguity about the exact scheme scope. It does not explicitly distinguish itself from the sibling tool get_bulgarian_pension_fund_managers, but the core purpose is unambiguous.
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 provides explicit when-to-use guidance: 'Use this to discover licensed managers and their supported scheme types before running rankings or metric calculations over FSC-tracked pension data.' It does not mention when not to use it or name alternative tools, but the intended context is clear and actionable.
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.
Many tools have identical aliases (e.g., list_funds and get_bulgarian_pension_funds, list_benchmarks and get_bulgarian_pension_benchmarks), creating ambiguity. An agent would struggle to choose between them. Additionally, cache_stats is unrelated to the core domain, adding confusion.
Naming patterns are inconsistent: some tools use short verb_noun (list_funds, compute_metric), while aliases are long and verbose (get_bulgarian_pension_fund_managers). Mixing both styles without clear distinction harms predictability.
26 tools is on the high side, but many are aliases; the unique tool count is around 16-17, which is reasonable for a comprehensive analytics server. However, the alias redundancy makes the list feel bloated.
The tool set covers discovery (list_funds, list_managers, list_benchmarks), data retrieval (get_nav_series, get_holdings_reports_index), computation (compute_metric, rank), simulation (simulate_saver_outcome), and legal documents (search_pension_law). Missing are tools for updating or creating data, which is acceptable for an analytics server. A minor gap is the lack of a direct fund detail tool besides NAV.
