us-macro-mcp

by hgus107

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Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
Each tool has a distinct purpose: listing, current value, history, comparison, calendar, snapshot, recession signals, and search. No overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, e.g., list_indicators, get_history, compare_indicators.
Tool Count5/5
8 tools is ideal for the US macroeconomics domain, covering discovery, retrieval, comparison, and specialized checks without excess.
Completeness5/5
The set covers all key operations: discovery, single value, history, multi-indicator comparison, future releases, full snapshot, recession signals, and search. No obvious gaps.
Average 4.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on July 28, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries full burden. Discloses that it returns latest value and recent change, but does not define 'recent change' (time frame), data sources, or update frequency. Some ambiguity remains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise single-sentence purpose followed by clear Args section. Efficient and well-structured, though could be slightly more streamlined.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists, so return values need not be described. Covers purpose and parameter adequately. Lacks clarification on 'recent change' but sufficient for a snapshot tool.
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?
Input schema has 0% description coverage, but the description explains the single parameter 'category', listing valid values like 'all', inflation, employment, etc. Adds meaning beyond the schema.
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?
Clearly states it gets a current snapshot of the US economy with latest value and recent change for every headline indicator. Distinguishes from sibling tools that focus on specific indicators or historical data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for broad economic overview, but does not explicitly state when not to use or mention alternatives. However, sibling tool names provide context for tool separation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 discloses the output (plain-English status for each signal) but does not mention rate limits, auth needs, data sources, or update frequency. For a read-only tool with no parameters, this is adequate but could be improved.
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?
Single sentence, no superfluous content. Efficiently communicates purpose and included indicators.
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?
Given the tool has no parameters and an output schema exists, the description is sufficiently complete. It lists the signals covered, which aligns with the expected output.
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?
There are zero parameters, so schema coverage is 100%. The description adds value by enumerating the specific signals checked, which is more meaningful than the empty schema. Baseline for 0 params is 4.
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 clearly states the tool checks main US recession warning signals, lists specific indicators (Sahm rule, yield curve, etc.), and notes each has a plain-English status. This distinguishes it from siblings like list_indicators or macro_snapshot.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for a quick overview of multiple recession signals but does not explicitly state when to use or not use this tool, nor does it mention alternatives among the 7 sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. Discloses parameter defaults and 'most recent kept' for limit. Lacks details on error handling (invalid name, date range) and rate limits. Adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is structured with an Args section, concise without redundant words. Could be slightly more concise but is well-organized and front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and presence of output schema, description sufficiently explains input parameters and their behavior (defaults, format). Lacks output description but output schema is available. Adequately complete for basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must add meaning. It does so comprehensively: explains 'name' as friendly indicator name, date formats as YYYY-MM-DD, limit behavior, and defaults. Adds significant value beyond schema.
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?
Description clearly states 'Get the historical time series for one indicator' with a specific verb and resource. Distinguishes from siblings like list_indicators (listing) and compare_indicators (comparison).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes when to use the tool for time series data. Does not provide explicit when-not-to-use or alternative tool references, but the sibling context aids inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It states it lists indicators but does not mention idempotency, side effects, or response structure. However, an output schema exists, so return details are not required. Minimal but adequate for a simple read operation.
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?
Very concise: two sentences and one line for args. The first sentence states purpose, the second gives usage guidance, and the parameter is explained clearly. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool (one optional param, no required) and presence of output schema, the description is nearly complete. It explains purpose, usage order, and parameter options. Could mention that listing is all indicators without pagination, but not necessary.
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?
Schema description coverage is 0%, but the description compensates by explaining the 'category' parameter and listing valid values: inflation, employment, etc. This adds significant meaning beyond the schema which only defines type 'string or null'.
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?
Clearly states 'List every US economic indicator this server provides.' Identifies the specific verb (list) and resource (indicators). Differentiates from sibling tools by advising to call this first before using other tools like get_indicator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises 'Call this first to discover valid indicator names before using the other tools.' Provides guidance on when to use (before others) and optional filtering with category values. No explicit when-not-to-use, but strong usage context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It explains that the search covers indicator names, titles, and descriptions, but does not mention any additional behavioral traits such as pagination, ordering, or whether it's read-only. This is adequate but not comprehensive.
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?
The description is extremely concise at 4 lines, with a clear front-loaded purpose and parameter explanations. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only 2 parameters and an output schema exists, the description is complete enough. It explains the search behavior and parameters; no need to detail return values. It could mention ordering or pagination but is still adequate.
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 schema coverage is 0%, so the description must add meaning. It describes 'query' as free-text words across specific fields and 'limit' as max matches with a default of 10. This adds sufficient context beyond the bare schema titles.
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 clearly states the tool finds indicators by keyword and explicitly says it's for when you don't know the exact name, giving concrete examples like 'gas prices', 'wages'. This distinguishes it from sibling tools like list_indicators and get_indicator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'when you do not know the exact name', providing clear context for when to use this tool over others. However, it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 states the tool shows scheduled releases but does not explicitly disclose that it is read-only, nor does it mention any limitations or side effects. The default and max for days_ahead are noted, but more transparency (e.g., 'this is a read-only operation') would improve the score.
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?
The description is extremely concise with two sentences and parameter documentation. It front-loads the purpose and example, with no wasted words.
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?
Given the tool's simplicity (1 optional parameter, no required inputs, output schema exists), the description provides all necessary context. It covers the tool's purpose, the parameter, and provides an example query, making it 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.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It fully explains the 'days_ahead' parameter with purpose ('How many days forward to look'), default (14), and maximum (90), adding significant meaning beyond the schema's type and default.
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 specific verbs ('Show', 'scheduled') and clearly identifies the resource ('US economic data releases') and scope ('over the coming days'). The example query distinguishes it from sibling tools like list_indicators (list all indicators) and get_indicator (specific details).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the use case with an example question, implying when to use this tool. It does not explicitly state when not to use it, but the sibling tools provide clear alternatives, making the guidance sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description reveals the tool's purpose, parameters, and default behavior. It does not explicitly state that the tool is read-only or describe side effects, but the nature of comparison implies no destructive actions. Additional detail on return format could be added.
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?
The description is concise, front-loaded with the core purpose, and uses a clear docstring format for parameters. Every sentence adds value without 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?
Given the tool's moderate complexity, the presence of an output schema, and no annotations, the description covers all necessary aspects: what it does, parameters with examples, and default behavior. It is complete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the schema: it explains 'names' are friendly indicator names with examples, and 'period' includes allowed values ('1y', '2y', '5y', '10y') and default. Since schema coverage is 0%, this is essential and well-done.
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 clearly states it compares two or more indicators over the same period, with concrete examples like 'did wage growth beat inflation'. It distinguishes from sibling tools such as 'list_indicators' or 'get_indicator' by focusing on comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('for questions like...'), implying comparative analysis. It does not explicitly state when not to use or list alternatives, but the examples and scope give sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a read-only operation (gets latest value) and mentions recent changes. No annotations are present, so the description carries the full burden; it is clear but lacks details on rate limits or permissions.
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?
The description is concise (two sentences plus parameter detail) and front-loaded with the purpose, with no unnecessary words.
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?
Given the tool's simplicity, one parameter, and the presence of an output schema, the description adequately covers the tool's functionality and context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description fully explains the 'name' parameter with examples and a reference to list_indicators, adding significant meaning beyond the schema's minimal title.
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 clearly states the tool gets the latest value of one economic indicator with recent changes, differentiating it from siblings like get_history (historical data) and compare_indicators (comparison).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions using list_indicators to see all names, providing a prerequisite. However, it does not explicitly compare when to use this tool vs other similar tools like get_history or compare_indicators.
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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