treasury_federal_net_costUS federal government net cost / spending by agency (gross cost, earned revenue, net cost) from the Treasury statement_net_cost dataset. Returns recent records; optionally filter by fiscal_year.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of records to return (default 20) | |
| fiscal_year | No | Four-digit fiscal year to filter by (e.g., "2024"). Omit for most recent. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "limit": 20
+ },
+ {
+ "fiscal_year": "2024",
+ "limit": 20
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so safety and idempotency are clear. The description adds context about returning 'recent records' and the dataset name, but does not disclose any behavioral traits beyond what annotations provide.
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, well-structured sentence that conveys the tool's purpose immediately. Every word is meaningful and front-loaded.
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?
Despite no output schema, the description explains the key fields returned (gross cost, earned revenue, net cost) and the dataset. For a simple read tool with two optional parameters, this provides sufficient 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 coverage is 100%, so the schema already describes both parameters (limit and fiscal_year). The description mentions optional fiscal_year filtering, but adds no additional meaning beyond the schema.
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?
Description clearly states the tool returns US federal government net cost by agency, listing the data fields (gross cost, earned revenue, net cost) and the dataset source. This distinguishes it from sibling treasury tools focused on other metrics like interest rates or receipts.
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 implicitly suggests using this tool for net cost data by agency, but does not explicitly state when to use it versus alternatives like treasury_debt or treasury_receipts. No exclusions or prerequisites are provided.
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.
Most of the surface is dominated by overlapping meta-tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) plus paired helpers (discover_tools/suggest_questions, ai_visibility_check/scan_competitor_ai_presence), so an agent can easily pick the wrong one. The seven treasury_* tools are clearly distinct, but they are a small island in a much larger ambiguous set.
Names are broadly snake_case and family-prefixed (treasury_*, polymarket_*, pipeworx_*), which helps, but the pattern is not consistently verb_noun: ask_pipeworx, bet_research, entity_profile, deep_research, search_within, and recent_changes mix verb, noun, and product-specific naming styles. Within families it is readable, but across the whole set it is inconsistent.
37 tools is too many for a server whose label is 'Treasury Fiscal'; only about six tools are treasury-specific and the rest are unrelated research, betting, memory, and subscription utilities. This is well into the 25+ heavy range and would make tool selection expensive and error-prone.
For a Treasury/Fiscal server the surface is thin: debt, receipts, customs duty, average rates, exchange rates, and net cost cover only a slice of Treasury data. Missing obvious components such as daily yield curves, auction calendars/results, federal outlays/spending by agency or function, and tax or appropriations data; the broad ask_pipeworx router softens but does not fill these as first-class tools.