List Bulgarian private pension funds and canonical fund IDs.
Returns fund_id in manager_slug:scheme_code format for UPF, PPF, and VPF/DPF.
Use this as discovery before calling NAV, metric, and ranking tools.
Data freshness: fund metadata follows latest FSC-tracked manager/scheme dataset.
| Name | Required | Description | Default |
|---|---|---|---|
| active_only | No | ||
| 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 bears the full burden. It discloses the return format of `fund_id` and data freshness, which is useful context. However, it does not mention filtering behavior (e.g., active_only default) or potential side effects, though as a read-only list tool, side effects are minimal. The description adds some value but lacks comprehensive behavioral detail.
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 three sentences, each providing essential value: the core purpose, usage guidance, and data freshness. No redundant or filler content; it is tightly written and well-structured.
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?
With an output schema present, return-value details are not needed. The description covers the tool's purpose, usage, and data freshness, which is sufficient for a list/discovery tool. It could mention how parameters affect the result, but the schema defaults and enums provide partial clarity.
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 does not explain the two parameters. It mentions UPF, PPF, and VPF/DPF, which implicitly relate to `scheme_code`, but it does not clarify that `scheme_code` filters by fund type or explain `active_only`. The description fails to compensate for the schema's lack of parameter documentation.
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 lists Bulgarian private pension funds and canonical fund IDs, using a specific verb and resource. It distinguishes itself from sibling tools by highlighting the `fund_id` format and its role as a discovery tool before NAV, metric, and ranking tools.
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 'Use this as discovery before calling NAV, metric, and ranking tools,' providing clear when-to-use guidance. It does not explicitly mention alternatives or exclusions, but the discovery role is well-defined and sufficient for guiding the 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.
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.
