treasury-fiscaldata-mcp-server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: what each entry surfaces (source tool, query parameters, timestamps, row count, column schema) and a prerequisite (CANVAS_PROVIDER_TYPE=duckdb). This goes beyond the annotations and enriches the agent's understanding of the tool's behavior without contradiction.

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?

Four tight sentences, each earning its place: the action/resource, the entry contents, the usage directive, and the environment prerequisite. The critical usage guidance is front-loaded, and there is no redundant or filler language.

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?

For a listing/discovery tool with an optional parameter and an existing output schema, the description covers all operational essentials: what it lists, how to narrow to a single dataframe, the metadata available, the correct usage sequence, and the required environment. Nothing an agent needs to invoke it correctly is missing.

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 input schema has 100% schema description coverage, fully documenting the optional 'name' parameter with its format (df_XXXXX_XXXXX) and behavior (describe single vs. list all). The description adds no extra semantic beyond what the schema already states, so the baseline of 3 is appropriate.

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 uses a specific verb ('List') and resource ('DataCanvas dataframes') and ties them to the exact source tools that materialize them. It also differentiates from the sibling tool treasury_dataframe_query by stating its intended role as a discovery step, making the purpose unmistakable.

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?

Explicitly states when to use: 'Use this tool before treasury_dataframe_query to discover table names and column types.' It implies the alternative (treasury_dataframe_query) and provides a clear sequencing directive, leaving no ambiguity about its place in the workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true. The description goes further by revealing that all columns are VARCHAR (requiring CAST), that system catalogs are denied at the bridge layer, and that row_count_capped can be true when exceeding row_limit. This adds behavioral context beyond the annotations, such as the VARCHAR contract and query cap behavior, making the tool's execution semantics clear.

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 carrying weight: the first defines the action and scope, the second lists restrictions, and the third gives typing guidance and recommends the describe tool. It is front-loaded with the core purpose, then constraints, then usage tip. No filler; every sentence earns its place. This is optimally concise for the information conveyed.

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's complexity (SQL execution with restrictions, multiple parameters, and a need for schema discovery), the description covers all essential aspects: what it does, safety constraints, VARCHAR caveat, how to discover available tables, and real-world usage via an example. The output schema exists, so return values are documented elsewhere. The description leaves no critical gaps for an agent to call the tool correctly and interpret results (e.g., casting, row caps).

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 100%, so the schema already documents all four parameters. The description adds value by reinforcing the VARCHAR casting requirement and providing an example SQL statement that demonstrates the CAST pattern. While the description repeats some schema details (e.g., row_limit cap), it enhances understanding of how parameters interact (preview vs. row_limit vs. register_as) and offers a concrete usage example. This is above the baseline of 3 because it adds context that helps avoid misuse.

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 identifies the tool's action: 'Run a single-statement SELECT against DataCanvas dataframes'. It names the specific registration tools (treasury_query_dataset, treasury_get_debt, etc.) and explicitly distinguishes from treasury_dataframe_describe by telling users to call that tool for table listing. The read-only constraint and denial of DDL/DROP/etc. further pin down the scope, leaving no ambiguity about what this tool does and what it is not.

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 provides explicit usage direction: 'Use treasury_dataframe_describe to list available table names and column schemas before querying.' It also explains when to avoid the tool (writes/DDL are rejected) and gives practical tips like casting VARCHAR to DECIMAL/DATE for arithmetic and date comparisons. This is clear, actionable guidance that helps an agent decide when to use this tool and how to use it correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnlyHint=true, idempotentHint=true), the description discloses meaningful behavioral details: the automatic staging of large series results into DataCanvas tables, the requirement for CANVAS_PROVIDER_TYPE=duckdb, the server-side table naming, and the fact that only business days are recorded. None of these contradict the annotations, and they substantially enrich the agent's understanding of side effects and constraints.

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 long but every clause earns its place given the tool's complexity. It front-loads the core purpose and mode summary before diving into staging details, and it uses clear punctuation to separate ideas. The only minor quibble is that the staging explanation could be tightened, but it remains tightly structured and not verbose.

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 available, the description doesn't need to cover return values. It provides all necessary context for correct invocation: mode semantics, date constraints, default behavior for missing start/end dates, automatic staging rules, and pointers to related tools. An agent can confidently call this tool without additional information.

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?

Although the schema provides 100% parameter coverage, the description adds significant operational meaning: it explains the interplay of mode with start_date/end_date, clarifies that canvas_id only requests staging ('the server picks the table name'), and highlights the default for end_date and the historical depth (1993-04-01). This goes well beyond the schema's individual field descriptions.

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 states a specific verb-resource pair ('Fetch national debt') and immediately differentiates the tool from siblings by naming the exact data source ('Debt to the Penny') and the breakdown into publicly-held debt and intragovernmental holdings. It also enumerates three distinct modes, making the tool's purpose unambiguous and distinct from other treasury 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 gives explicit, actionable guidance for each mode: 'latest' for the most recent business day, 'date' for a specific business day, and 'series' for a range. It also explains when to use the DataCanvas staging (canvas_id or >500 rows) and routes the agent to treasury_dataframe_describe and treasury_dataframe_query for further analysis. It even notes the business-day constraint and date range origin, leaving no ambiguity about when the tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint and idempotentHint, but the description goes well beyond by explaining that these are not market rates, not for transaction pricing, published quarterly, and that 'series' results auto-stage when they exceed 500 rows. This significantly enriches the agent's understanding of side effects and constraints.

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 longer than typical but every sentence contributes critical information (purpose, schedule, mode semantics, rate interpretation, warning, staging behavior). It's front-loaded with the primary purpose and ends with actionable follow-up steps. Slightly dense but not wasteful.

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?

The description is comprehensive for a 5-parameter tool with an output schema: it explains both modes, the rate format, the non-market-rate caveat, the staging trigger, and the required follow-up tools. The agent can determine exactly when to use this tool and how to process results without external guesswork.

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 input schema already covers all parameters with detailed descriptions (100% coverage), so the baseline is 3. The description adds value by interpreting the mode parameter (latest returns most recent quarter, series returns date range), giving a concrete rate expression example, and clarifying the canvas_id staging condition. This goes beyond simple schema repetition.

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 returns official Treasury exchange rates for ~165 countries, specifies the reporting purpose, and differentiates from sibling tools like treasury_get_debt and treasury_get_interest_rates. The verb 'get' and resource 'exchange rates' are explicit, and the context of federal agency reporting adds precision.

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 explains when to use 'latest' vs 'series' modes and provides specific guidance for handling large series results via DataCanvas staging, including naming the sibling tools to read and query the staged table. It doesn't explicitly contrast with all sibling tools but gives enough context to choose correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover read-only and idempotent behavior, so the bar is lower. The description adds substantial behavioral nuance: rates are percentages not basis points, the set of security types changes over years, data is updated monthly, and large series trigger automatic DataCanvas staging. It also discloses that canvas_id staging requires duckdb. These details go far beyond the annotations and significantly aid correct invocation.

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 dense but well-structured, starting with the core purpose and then layering details about coverage, units, update frequency, and modes. Every sentence contributes necessary information; the length is justified given the tool's complexity (5 params, two modes, staging behavior). It could be slightly trimmed in wording, but it remains focused and front-loaded with the most important facts.

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's complexity and that an output schema exists, the description covers all essential operational aspects: how to use modes, parameter nuances, staging trigger conditions, and the follow-up workflow with sibling tools. It even explains the 'omit security_type' strategy for discovering available types. Nothing an agent needs to call it correctly and interpret results is missing.

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?

Although schema coverage is 100% (all parameters documented), the description adds crucial semantic depth: security_type can be omitted to list all types and the response names types when a filter matches nothing; start_date should be end-of-month; canvas_id only requests staging and the actual table name is returned; the exact match requirement for security_type is clarified. These enrich the schema descriptions to a level that prevents common errors.

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's function: obtaining average interest rates on Treasury securities by type, with a specific verb and resource. It explicitly differentiates from siblings by naming the domain (interest rates vs debt, exchange rates) and covers the full scope of security types. The inclusion of a clarifying 'answers the government's cost of borrowing' makes the purpose immediately obvious.

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 provides explicit guidance on how to use the tool's modes ('latest' vs 'series'), when to omit security_type to discover available types, and the meaning of end-of-month for start_date. It also references sibling tools (treasury_dataframe_describe, treasury_dataframe_query) for post-processing staged results. However, it does not explicitly state when to choose this tool over alternatives like treasury_get_debt or treasury_get_exchange_rates, leaving that to the agent's domain inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered without description help. The description adds meaningful context beyond that: the catalog is a curated subset (so omission is expected behavior, not a bug), and the 400-error failure mode from upstream. It does not contradict the annotations.

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?

Four sentences, each earning its place: purpose and return content, then usage guidance, then the curated-subset caveat. Front-loaded with the most important information and zero filler or repetition of the schema/annotations.

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?

The tool is simple (2 optional params, 0 required, both fully documented) and has an output schema, so return values need no elaboration. The description covers what it returns, when to use it, the important incompleteness caveat, and failure behavior — nothing an agent needs to call it correctly is missing.

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 100% — both 'search' (case-insensitive substring match) and 'category' (enumerated options, omit to list all) are fully documented in the schema. The description adds no parameter-specific detail beyond the schema, so it rests at the baseline 3 rather than compensating for any coverage gap.

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?

Uses a specific verb ('Browse') with a clear resource ('curated catalog of US Treasury Fiscal Data API endpoints') and details the return content: endpoint paths, field names, descriptions, and update cadence. It explicitly differentiates itself from treasury_query_dataset as the listing vs. querying counterpart, so an agent can distinguish it from all six siblings without opening schemas.

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?

States explicitly to use this tool before treasury_query_dataset to discover correct endpoint paths and field names, warning that a typo in either causes a 400 error. It also gives a when-not condition: since the catalog is curated/subset, pass endpoint paths directly to treasury_query_dataset for unlisted datasets. Both the primary use and the alternative path are spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark readOnlyHint and idempotentHint, so the description needn't repeat safety. It goes beyond by revealing that 'All response values are strings per the API contract, including numbers and dates; "null" (string) means no value,' a crucial behavioral detail. It also discloses a 400 on typos and the CANVAS_PROVIDER_TYPE=duckdb prerequisite, enriching the agent's mental model.

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?

Though long, every sentence serves a purpose: core function, prerequisite, syntax, response behavior, and workflow. It is front-loaded with the primary action and dependency, then details. No fluff or repetition of schema defaults; structure is logical and scannable.

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's complexity (7 parameters, multiple modes), the description covers endpoint discovery, field precision, filter grammar, sorting, pagination via page_number/total_pages, and the canvas staging path. The output schema handles return structure, so nothing an agent needs to call correctly is missing; the required duckdb context and string-typed responses are supplied.

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 baseline is 3, but the description elevates it by explaining the filter syntax as '{ field, operator, value }' with operator enum and an example ('record_date:gte:2024-01-01'), stating conditions are ANDed. It also clarifies canvas_id's staging role and the flow to treasury_dataframe_describe/query, which the schema alone does not convey, adding genuine semantic value.

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 opens with 'Query any Treasury Fiscal Data endpoint by path, field list, filters, sort, and page,' clearly stating a specific action (query) on a defined resource (Treasury Fiscal Data endpoints) with explicit scope. It differentiates from siblings by naming treasury_list_datasets for endpoint discovery and treasury_dataframe_describe/query for downstream data canvas operations, leaving no ambiguity about what this tool is for.

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?

Explicitly instructs 'Call treasury_list_datasets first to get the correct endpoint path and exact field names — a typo in either causes a 400,' giving a hard dependency. It also advises on large pulls to 'use canvas_id with treasury_dataframe_query instead,' and explains the duckdb server requirement, so the agent knows when to use this tool versus alternatives and how to chain it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.