American Default Research
Server Details
Read-only MCP for U.S. household distress: 96 indicators, ADI composite, 3,144 counties.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- vibecode1/american-default-mcp
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.6/5 across 5 of 5 tools scored.
Each tool targets a distinct aspect of the American Default Research data: composite index, county scorecard, cross-correlations, individual indicator, and search. No overlap in functionality.
All tools follow a consistent `get_` or `search_` prefix with descriptive noun phrases (e.g., `get_adi_composite`, `search_indicators`). No mixing of conventions.
With 5 tools, the server is well-scoped for its purpose—covering discovery, retrieval of composites, county data, cross-correlations, and individual indicators without bloat or deficiency.
The tool surface covers core research needs (search, indicator details, composite, county-level, relationships). The only minor gap is direct access to raw historical series, but it is provided via an external link.
Available Tools
5 toolsget_adi_compositeARead-onlyIdempotentInspect
Fetch the latest quarterly reading of the American Distress Index (ADI) composite. Returns composite score (0-100), zone, composite Z-score, and the 5-component breakdown (Buffer, Debt Stress, Financial Conditions, Cost Pressure, Labor) with Z-scores and point contributions. Updated quarterly.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the tool is safe and idempotent. The description adds value by detailing the return format (composite score, zone, Z-score, component breakdown) and update frequency ('Updated quarterly'), providing behavioral context beyond 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?
The description is extremely concise at two sentences. It front-loads the action and resource, then lists return fields and update cadence. No superfluous words, every sentence earns its place.
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 zero parameters, no output schema, and annotations covering safety, the description is complete. It specifies what is returned (including 5 component breakdown) and that data is updated quarterly, which is sufficient for an agent to use the tool correctly.
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 tool has zero parameters, so the description cannot add parameter information. Per rubric, 0 parameters yields a baseline score of 4.
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 it 'Fetch the latest quarterly reading of the American Distress Index (ADI) composite,' specifying the exact resource and action. The tool name and sibling list (e.g., get_county_scorecard, get_indicator) further distinguish it as a composite-specific fetch, making purpose unambiguous.
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 tells when to use this tool (to get the ADI composite) but does not explicitly state when not to use it or name alternatives. Given sibling tool names, context is clear, but explicit guidance is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_county_scorecardARead-onlyIdempotentInspect
Fetch a county's County Distress Index (CDI) scorecard by 5-digit FIPS code. Returns composite score (0-100), zone (Healthy / Normal / Elevated / Serious / Crisis), national + state rank, 5-domain breakdown (Consumer Credit Distress, Housing Cost Burden, Structural Poverty, Economic Vitality, Legal Distress), key findings, and pre-baked APA / MLA / Chicago / news-copy citations. Accepts 4-digit FIPS with implicit leading zero. 3,144 counties available.
| Name | Required | Description | Default |
|---|---|---|---|
| fips | Yes | The 5-digit numeric county FIPS code as a string (e.g., '13063' for Clayton County, GA). 4-digit values are zero-padded internally. 3,144 counties available. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds value by detailing the return structure (score range, zone, ranks, domains, citations) and the FIPS handling, which provides context beyond 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?
The description is a single, well-organized paragraph that front-loads the main action and then enumerates key outputs. It is concise without being terse, and every sentence provides useful information.
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 no output schema, the description fully explains the return values (composite score, zone, ranks, domain breakdown, key findings, citations) and addresses input edge cases (4-digit FIPS). It is complete for a tool with one simple parameter.
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% and already describes the fips parameter format and count. The description repeats this and adds a minor clarification (accepts 4-digit, zero-padded), which is helpful but not significantly 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?
The description clearly states the verb-resource pair 'Fetch a county's County Distress Index (CDI) scorecard' and distinguishes from siblings by listing specific outputs like composite score, zone, ranks, domain breakdown, and citations.
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 (to get a county's CDI scorecard) and provides input tips (4-digit FIPS accepted with implicit leading zero), but does not explicitly contrast with sibling tools or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cross_correlationsARead-onlyIdempotentInspect
Fetch statistically-validated leading/lagging relationships for an indicator. Source: the five-filter leading-indicator scanner (cross-correlation → first-differenced CCF → multi-crisis validation → Granger causality → out-of-sample validation). Returns two lists: as_leader (pairs where this indicator precedes its follower) and as_follower (pairs where another indicator precedes this one). Only fully-validated pairs are included — partial matches are not surfaced. Most of the 96 indicators return empty lists; currently six pairs clear the full gauntlet.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The kebab-case indicator slug to look up (e.g., 'the-buffer', 'initial-unemployment-claims-sa'). Returns split lists of pairs where this indicator is the leader vs the follower. Empty lists are common — only fully-validated pairs surface. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral details: only fully-validated pairs are returned, partial matches are excluded, and most indicators return empty lists. No contradiction with 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?
The description is concise (three sentences) with the main purpose front-loaded. Every sentence provides valuable context without redundancy.
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 compensates by detailing the return format (as_leader, as_follower) and expectations (empty lists are common). The tool is simple (1 parameter), and the description is fully adequate.
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 schema description for the slug parameter is complete (100% coverage) and includes examples. The description further clarifies the return structure and common behavior (empty lists), adding 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?
The description clearly states the tool fetches statistically-validated leading/lagging relationships for an indicator, specifying the source and the two return lists. It distinguishes from sibling tools like get_indicator and search_indicators by focusing on correlations.
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 this tool (to get leading/lagging relationships for a specific indicator) and sets realistic expectations about empty results. It implicitly suggests alternatives for general indicator data but does not explicitly 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.
get_indicatorARead-onlyIdempotentInspect
Fetch a compact snapshot of an American Default economic indicator by slug. Returns latest value, unit, frequency, direction, pre-computed aggregates (period averages, extremes, sustained runs), editorial prose (when available), and canonical APA / MLA / Chicago / news-copy citations. Raw historical series is NOT included — use https://americandefault.org/api/indicators/{slug}.json for the full data. Slug examples: 'the-buffer' (personal savings rate), 'mortgage-delinquency', 'initial-unemployment-claims-sa'.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The kebab-case indicator slug (e.g., 'the-buffer' for personal savings rate, 'mortgage-delinquency', 'initial-unemployment-claims-sa'). 96 indicators available. Use `search_indicators` first to discover which slug corresponds to a concept. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only, idempotent, non-destructive behavior. The description adds detail on what is returned (latest value, unit, frequency, etc.) and crucially states what is NOT included (raw historical series). No 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?
The description is concise, with no wasted words. Key information is front-loaded, and every sentence contributes value.
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 simplicity (one required parameter, no output schema), the description is complete. It explains what the return value includes and explicitly states what is excluded, leaving no ambiguity.
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% with a single 'slug' parameter. The description adds examples and guidance to use search_indicators first, which enriches the schema's minimal description.
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 fetches a 'compact snapshot' of an economic indicator by slug. It distinguishes from the raw historical series by explicitly noting what is not included, and references sibling tool 'search_indicators' for slug discovery.
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 context on when to use this tool (for compact snapshot) and suggests using search_indicators first to find the slug. It also points to an alternative source for raw data. However, it does not explicitly compare with other siblings like get_adi_composite or get_county_scorecard.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_indicatorsARead-onlyIdempotentInspect
Search the 96-indicator registry by keyword. Returns ranked matches (up to limit, default 10, max 50) with slug, branded name, underlying name, category, and canonical URL. Scoring is substring+prefix over slug, branded_name, name, and category — e.g. query 'savings' returns both The Buffer (personal saving rate) and The Safety Net (emergency savings survey). Use this when you want to discover which slug corresponds to a concept before calling get_indicator.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max number of ranked matches to return. Default 10. Hard cap 50 (values above are clamped). Values above 1000 are rejected as abuse. | |
| query | Yes | The keyword to search (e.g., 'savings', 'mortgage', 'inflation'). Scoring runs substring + prefix matching over slug, branded_name, name, and category fields. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds details on scoring (substring+prefix matching), examples, and hard cap behavior (clamping values above 50, rejecting above 1000). No contradiction with 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?
Three sentences, front-loaded with purpose, then scoring details, then usage guidance. Every sentence is informative without redundancy.
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 simplicity and clear parameters, the description covers behavior, limits, and usage guidance. No output schema needed as return values are intuitive.
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 parameters are already well-documented. Description adds context on scoring logic and example usage but does not significantly extend beyond 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 it searches a 96-indicator registry by keyword and returns ranked matches with specific fields. It distinguishes from siblings by mentioning it is a discovery tool before calling get_indicator.
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 says 'Use this when you want to discover which slug corresponds to a concept before calling get_indicator.' This provides clear when-to-use and implies alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!