Alias for get_benchmark_series to retrieve inflation/market comparators for UPF/PPF/VPF.
| Name | Required | Description | Default |
|---|---|---|---|
| date_to | No | ||
| date_from | No | ||
| frequency | No | daily | |
| benchmark_slug | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only says 'retrieve', implying a read-only operation, but provides no details on return format, potential errors, rate limits, or inherited behavior from the aliased tool. The alias reference gives some context but is insufficient without further elaboration.
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 a single, concise sentence with no fluff. It is well-structured and immediately communicates the core purpose without unnecessary detail.
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 tool has 4 parameters, no annotations, and only a minimal description, it is insufficiently complete for reliable autonomous use. The alias reference and domain context help, but the description omits critical operational details such as how to find valid benchmark slugs, default date ranges, or the meaning of frequency options. The presence of an output schema partially compensates, but the overall completeness is low.
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 description adds minimal context about what benchmark_slug represents (inflation/market comparators), but it does not explain the date_from, date_to, or frequency parameters. With schema coverage at 0%, the description fails to compensate for parameter ambiguity, leaving agents to guess at valid input formats and slug values.
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 this is an alias for get_benchmark_series and clarifies the purpose: retrieving inflation/market comparators for UPF/PPF/VPF. It identifies the resource type (benchmark series) and the Bulgarian pension context, distinguishing it from the more general sibling tool.
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?
No explicit guidance on when to use this tool versus alternatives. It mentions being an alias for get_benchmark_series but does not explain when to prefer this over the general tool or other benchmark-related siblings. The usage context is implied (Bulgarian pension benchmarks) but not explicitly stated.
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.
