Alias for simulate_saver_outcome with explicit Bulgarian pension savings semantics.
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | No | ||
| date_from | No | ||
| scheme_code | No | upf | |
| manager_slug | No | ||
| monthly_amount | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, and the description provides no information about behavior such as whether it is read-only, returns data, has side effects, or requires authentication. The description is purely an alias note with zero behavioral disclosure.
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 at one sentence and is front-loaded with the alias information. However, it is under-specified and doesn't earn its place because it adds little value beyond what the name already conveys.
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 5 parameters, no annotations, and an output schema, this description is grossly incomplete. It fails to explain the tool's functionality, parameter meanings, return values, or how it differs from the sibling tool beyond being an alias, leaving the agent without critical context.
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 mention any parameters or their meaning. The agent gets no help understanding what `monthly_amount`, `scheme_code`, `date_from`, `end_date`, or `manager_slug` represent or how they affect the simulation.
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 merely says it is an alias for `simulate_saver_outcome` with Bulgarian semantics. This largely restates the tool's name without explaining what the tool actually does, such as how it simulates pension saver outcomes or what parameters affect the result.
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?
It implies usage for Bulgarian pension savings scenarios by mentioning 'explicit Bulgarian pension savings semantics' and names the alternative `simulate_saver_outcome`. However, it does not explicitly state when to use this over the generic tool or provide any exclusion criteria.
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.
