list_metrics
List all supported financial metric identifiers for MCP analytics tools.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Pensiata - Bulgarian Pension Fund Analytics
TDQS
A3.9/5.0
Behavior3/5
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 clearly implies a read-only operation by using 'List' and specifies the scope ('for MCP analytics tools'). However, it doesn't disclose potential size of the list, whether it includes all metrics across datasets, or if any special considerations apply.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the action ('List') and immediately states the resource. Every word is informative, with no verbosity or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (no parameters, output schema present). The description fully conveys what the tool does and its scope. Given the presence of an output schema, there is no need to explain return values. This is complete for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema provides no parameter semantics. The description adds value by clarifying that the output will be metric identifiers, which is exactly the type of information an agent needs. With 0 params, baseline is 4, and the description earns it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'List' with a clear resource: 'all supported financial metric identifiers for MCP analytics tools'. It clearly distinguishes from sibling list_* tools (list_benchmarks, list_funds, list_managers) by focusing on metrics, and from get_bulgarian_pension_metrics_catalog by being general rather than Bulgarian-specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 doesn't mention that this might be used to discover valid metric identifiers before calling compute_metric or other metric-consuming tools, nor does it clarify the distinction from get_bulgarian_pension_metrics_catalog.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
C2.9/5.0
Disambiguation2/5
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 Consistency2/5
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.
Tool Count3/5
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.
Completeness4/5
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.