Real-time labor market intelligence from 6 federal datasets. Track mass layoffs, visa petitions, SEC restructuring filings, bankruptcy cases, unemployment claims, and job market turnover across all 50 US states. Includes proprietary risk signal scoring that cross-references WARN + SEC + bankruptcy data.
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.
Average 4.1/5 across 12 of 12 tools scored. Lowest: 3.1/5.
Several tools have overlapping scopes: get_company_layoffs vs search_layoffs (company search), get_state_summary vs get_state_intelligence (state-level view), and get_market_pulse vs get_stats (overall statistics). While descriptions clarify some differences, an agent could easily misselect between these pairs.
All tool names follow a verb_noun snake_case pattern, with the majority starting with 'get_' and the rest being clear single verbs (export_records, search_layoffs, authenticate). The naming is consistent and predictable.
12 tools is well within the ideal 3-15 range. Each tool serves a discrete purpose (auth, export, queries, analytics, pricing, stats), and the count feels appropriate for a comprehensive data API.
The tool set covers querying WARN data by various dimensions, bulk export, cross-dataset analytics, and meta operations. Minor gaps include direct access to individual non-WARN datasets and a tool to fetch a single layoff notice by ID, but these are workarounds via search or export.
authenticateAInspectAuthenticate with your WARN Firehose API key. Returns your tier and access level.
Args:
api_key: Your WARN Firehose API key (starts with wf_)
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral disclosure. It states that authentication returns tier/access level and that the key must start with 'wf_', which adds useful context. However, it does not disclose error behavior, session effects, or whether the key is validated against the server until invocation. This is adequate but not rich, warranting a mid-range score.
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 two sentences plus a well-formatted Args block. Every sentence earns its place: the first states the action and return, the second describes the parameter. No wasted words; structure is clear and front-loaded with the core purpose.
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 one-parameter tool with no output schema, the description covers the essential context: what to provide (api_key) and what to expect (tier and access level). It lacks error handling or repeat-call guidance, but given the simplicity, it is sufficiently complete. A perfect score would require more operational detail, so a 4 is appropriate.
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 declares only 'api_key' as a required string with no description (0% schema coverage). The description compensates by explaining the parameter: 'Your WARN Firehose API key (starts with wf_)'. This adds meaningful format and ownership context beyond the schema. It could go further (e.g., where to obtain the key), but the provided hint is valuable.
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: 'Authenticate with your WARN Firehose API key.' It uses a specific verb ('Authenticate') and resource ('API key'), and it distinguishes itself from sibling data-retrieval tools by being an authentication utility. It also mentions the return value (tier and access level), reinforcing its purpose.
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 implies this is a prerequisite for other API interactions (authentication), but it does not explicitly state when to use it versus alternatives. Since all sibling tools are data queries, it is clear this is the auth gate, yet the description lacks explicit 'use this before other calls' guidance. Score reflects implied but not explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
export_recordsAInspectExport WARN records in bulk. Requires Pro tier or higher.
Returns up to 500 records with full field details. For CSV/Parquet
downloads, use the REST API at /api/export/.
Get your API key at warnfirehose.com/account
Args:
api_key: Your WARN Firehose API key (Pro tier required)
state: Optional 2-letter state code filter
company: Optional company name filter (partial match)
days: Look back this many days (default 90, max 730)
limit: Max records to return (default 100, max 500)
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No | ||
| state | No | ||
| api_key | Yes | ||
| company | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description takes on the full burden. It discloses the Pro tier requirement, API key necessity, maximum return size (500), and a boundary (no CSV/Parquet export, use REST API). While it lacks error handling or detailed return format, it covers key behavioral 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 well-structured with a lead sentence for purpose, followed by usage constraints and alternative, then a clean Args list. Every sentence adds value, and it is appropriately sized for 5 parameters.
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 covers purpose, authentication, limits, and a key alternative. With no output schema, it mentions 'full field details' but could be more specific about the return format or behavior when no records match. Still, it is reasonably complete for a bulk export tool with moderate complexity.
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 0%, and the description fully compensates by explaining each of the 5 parameters: api_key (Pro tier), state (2-letter code), company (partial match), days (lookback range), and limit (max value). This adds significant meaning beyond the bare 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 states a specific verb and resource: 'Export WARN records in bulk.' This clearly distinguishes the tool from siblings like search_layoffs or get_recent_layoffs by emphasizing bulk export capability.
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 usage context: requires Pro tier, returns up to 500 records, and directs users to the REST API for CSV/Parquet downloads as an alternative. It does not explicitly contrast with sibling functions, but the alternative is clearly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_company_layoffsAInspectGet all WARN Act layoff notices for a specific company.
Args:
company: Company name to search for (partial match supported)
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | ||
| company | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses partial match behavior and the optional api_key for higher rate limits. However, it doesn't mention pagination or response shaping, but as a read-only get, this is adequate.
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?
Two front-loaded sentences plus a compact Args list. No fluff, every sentence earns its place. The format is scannable and clear.
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 output schema exists, return values needn't be described. The tool is simple (2 params, no nesting) and the description covers purpose and parameters well. It could note when to choose this over search_layoffs, but overall it's complete enough.
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 descriptions are completely absent (0% coverage), but the description's Args section explicitly explains both parameters: company (partial match) and api_key (optional, higher rate limits). This fully compensates for the missing schema metadata.
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 ('Get') and resource ('WARN Act layoff notices') scoped to a specific company, and notes partial match support. This clearly differentiates it from sibling tools like search_layoffs (broader search) and get_recent_layoffs (time-based).
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 implies usage for company-specific lookups but does not explicitly state when not to use it or mention alternatives. It would benefit from a note like 'For broader searches, use search_layoffs instead.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_market_pulseAInspectGet a single-call market snapshot across all 6 datasets.
Returns: WARN stats (30d trend), top industries, at-risk companies,
LCA/H-1B counts, DOL claims, SEC filings, bankruptcies, JOLTS snapshot.
Args:
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the returned data and mentions api_key for higher rate limits, which is useful. However, it doesn't explicitly state whether the call is read-only, whether authentication is required for basic access, or any error/limitation behavior.
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 compact and well-structured, leading with the core action and using a bulleted list to summarize outputs. No unnecessary words or repetition.
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 complex snapshot tool, the description covers the main purpose, outputs, and the only parameter. Output schema exists, so extra return details are helpful but not required. It could mention limitations like data freshness, but it's sufficient for basic invocation.
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 provides only a bare optional string param with no description. The description explains 'api_key: Optional API key for higher rate limits', adding meaningful practical purpose 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 'Get a single-call market snapshot across all 6 datasets' and enumerates the return categories. This distinctly differentiates it from siblings like get_company_layoffs or get_recent_layoffs, which have narrower scope.
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 implies use when you need a broad cross-dataset overview in one call, but it does not explicitly state when not to use it or name alternative tools. There's no clear exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pricingAInspectReturn WARN Firehose pricing tiers, included tools, and signup URLs.
Use this when the user asks about pricing, hits a rate limit, or wants to upgrade. Returns tier prices, daily call limits, included datasets, and direct signup links so the user can act immediately.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the burden. It discloses what the tool returns (tier prices, daily call limits, included datasets, signup links) but does not mention any behavioral aspects like whether it makes network calls, requires authentication, or has side effects. For a simple read-only info tool, this is acceptable but not richly transparent.
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 two sentences with no fluff. The first sentence states the core purpose, and the second adds usage context and return details. It is front-loaded and every sentence earns its place. Excellent conciseness.
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 zero-parameter tool with no output schema, the description is fully complete. It covers what the tool does, when to use it, and what it returns. No additional information is needed for an agent to select and invoke it 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 schema coverage is trivially 100%. Per the rubric, a baseline of 4 is appropriate when there are no parameters. The description does not need to explain parameter meaning. It adds value by clarifying output details that are not in 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 'Return WARN Firehose pricing tiers, included tools, and signup URLs' with a specific verb and resource. This distinguishes it from sibling tools like get_company_layoffs or get_market_pulse, which focus on other domains. The purpose is 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 explicitly provides three trigger scenarios: 'when the user asks about pricing, hits a rate limit, or wants to upgrade.' This gives clear context for use. It does not mention when not to use or alternatives, but given the tool's unique purpose among siblings, that is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_recent_layoffsAInspectGet the most recent WARN Act layoff notices.
Args:
days: Look back this many days (default 30)
state: Optional state filter (2-letter code)
limit: Max results (default 25, max 100)
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No | ||
| state | No | ||
| api_key | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries some behavioral burden. It discloses the optional API key for higher rate limits, which is useful. However, it omits other behavioral details such as output format, sorting, pagination, or error handling. The read-only nature is implied by 'Get' but not explicitly stated.
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 appropriately sized: a one-line summary followed by a clear, structured Args list. Each parameter gets a concise explanation. No redundant or irrelevant text.
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 the existence of an output schema, the description adequately covers purpose and parameters. It does not explain return values (not needed due to output schema) but lacks some usage context and behavioral nuance that would make it fully complete for agent invocation.
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 0%, but the description explains every parameter: days (look back period), state (optional filter), limit (max results), and api_key (rate limits). This fully compensates for the schema's lack of descriptions and adds meaningful semantics.
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 'Get the most recent WARN Act layoff notices' with a specific verb and resource. While it doesn't explicitly differentiate from sibling tools like get_company_layoffs or search_layoffs, the focus on 'recent' and 'WARN Act' provides enough specificity for most use cases.
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?
There is no explicit guidance on when to use this tool versus alternatives like search_layoffs or get_company_layoffs. The description implies it's for recent notices but doesn't mention exclusions, prerequisites, or selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_risk_signalAInspectGet companies ranked by composite distress signal across all datasets.
Combines WARN layoff volume/recency, SEC restructuring filings,
bankruptcy filings, and H-1B denial rates into a single risk score.
Levels: Critical (7+), Elevated (4-6), Moderate (2-3), Low (1).
Requires Starter tier or higher. Get your API key at warnfirehose.com/account
Args:
state: Optional 2-letter state code to filter
min_score: Minimum risk score (default 3)
limit: Max results (default 15, max 50)
api_key: Your WARN Firehose API key (Starter tier required)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| state | No | ||
| api_key | No | ||
| min_score | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the composite signal construction, score levels (Critical, Elevated, Moderate, Low), and the authentication requirement. It doesn't mention rate limits or error behavior, but the core behavior and prerequisites are transparent.
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 front-loaded with the purpose, followed by a compact explanation of data sources and risk levels, then a structured Args list. Each sentence adds necessary information without wasted words, though it is slightly longer than strictly necessary.
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 presence of an output schema and the description covering data sources, scoring levels, authentication, and parameter semantics, the tool is sufficiently described for correct selection and invocation. Explicit comparisons with sibling tools would enhance completeness but are not essential.
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 0%, so the description is the only source of parameter meaning. The Args section defines each parameter: state as a 2-letter code, min_score as the minimum threshold, limit with max 50, and api_key tied to the Starter tier. This fully compensates for the lack of schema 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 clearly states the tool returns companies ranked by a composite distress signal, combining WARN layoffs, SEC restructuring, bankruptcy filings, and H-1B denial rates. This distinguishes it from sibling tools like get_company_layoffs or get_state_intelligence by highlighting its cross-dataset risk-scoring focus.
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 implies usage as a broad risk screening tool across multiple datasets, with clear context that it is not a raw data query. It mentions the Starter tier requirement but does not explicitly name alternatives or provide when-not-to-use guidance. The tier prerequisite adds practical usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_state_intelligenceAInspectGet a unified state profile combining all 6 datasets for a US state.
Returns WARN notices, LCA petitions, H-1B approvals/denials, DOL claims,
bankruptcy matches, JOLTS data, and a composite distress score.
Requires Pro tier or higher. Get your API key at warnfirehose.com/account
Args:
state_code: Two-letter state abbreviation (e.g. CA, TX, NY)
api_key: Your WARN Firehose API key (Pro tier required)
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | ||
| state_code | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral transparency. It discloses Pro tier auth requirement and lists the specific datasets returned, which adds context. However, it does not explicitly state that this is a read-only operation or address possible errors or rate limits, making it adequate but not exemplary.
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 compact and front-loaded with the core purpose, followed by return data, auth info, and args. No filler or redundant phrasing.
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 has an output schema, so return values don't need detailing. The description covers the data sources, auth requirement, and parameter meanings. The only minor gap is clarifying whether api_key is optional given the schema's default null, but overall it's complete for a data retrieval tool.
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 provides no descriptions, so the description fully compensates by explaining state_code as a two-letter abbreviation and api_key as the Pro tier requirement. Both parameters are meaningfully explained beyond the bare 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 'Get a unified state profile combining all 6 datasets for a US state,' which is a specific verb+resource. It also lists the datasets returned, but it does not explicitly differentiate from the sibling tool get_state_summary, so it stops short of a perfect 5.
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 implies this tool is for comprehensive state analysis by mentioning 'all 6 datasets' and the composite distress score, but it does not explicitly state when to use this tool versus simpler alternatives like get_state_summary, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_state_summaryAInspectGet a summary of WARN Act layoff data for a specific US state.
Args:
state: Two-letter state code (e.g. CA, TX, NY, FL)
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | ||
| api_key | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral burden. It implies a read operation ('Get') and mentions api_key for rate limits, but does not state whether any data is modified, whether authentication is required, or how errors (invalid state) are handled.
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 two sentences plus a clean parameter list. It is front-loaded with the purpose and every line adds value. No wasted words.
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 params, output schema exists), so the description is sufficient for invocation. It does not elaborate on return value details (covered by output schema) but lacks any notes on limitations or edge cases that might be relevant for a complete picture.
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 0%, so the description compensates by explaining both parameters: state as a two-letter code with examples, and api_key as optional for higher rate limits. This adds meaningful context beyond the raw 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 uses a specific verb ('Get') and identifies a concrete resource ('a summary of WARN Act layoff data') scoped to 'a specific US state'. This clearly differentiates from general query tools like search_layoffs.
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?
No guidance is provided on when to use this tool versus siblings like get_state_intelligence or search_layoffs. The description does not mention any exclusions or alternate tool recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_statsBInspectGet overall statistics about the WARN Firehose database.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It implies a read operation but does not explain what statistics are included, whether the api_key is required for authentication, potential rate limits, or any side effects. The description adds minimal transparency beyond the tool's basic purpose.
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, clear sentence with no wasted words. It is front-loaded with the action and resource, making it easy to scan.
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 tool with one optional parameter and an output schema, the description is very thin. It lacks context about what 'overall statistics' means, when to prefer this over specific data tools, and whether authentication is needed. The presence of many similar sibling tools makes this lack of context more problematic.
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 0%, and the description does not mention the api_key parameter. The parameter name itself is somewhat self-explanatory, but the description adds no meaning about whether the key is necessary or how it affects the statistics returned.
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 'Get' and the resource 'overall statistics about the WARN Firehose database,' which distinguishes it from sibling tools that focus on specific data like company layoffs or market pulse. The term 'overall statistics' conveys a distinct aggregate scope.
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?
No guidance is given on when to use this tool versus alternatives. Sibling tools like get_company_layoffs or get_recent_layoffs suggest more specific options, but the description does not mention them or provide any selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_talent_pipelineAInspectFind available talent from recent layoffs, cross-referenced with LCA visa roles.
Shows what occupations/skills each laid-off company was hiring for.
Useful for recruiters targeting skilled workers from recently laid-off companies.
Requires Starter tier or higher. Get your API key at warnfirehose.com/account
Args:
state: Optional 2-letter state code
days: Look back this many days (default 90)
limit: Max results (default 15, max 50)
api_key: Your WARN Firehose API key (Starter tier required)
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No | ||
| state | No | ||
| api_key | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the data source (recent layoffs + LCA visa roles), the type of output (occupations/skills per company), and the authentication/API key requirement. It does not explicitly state read-only behavior, but 'Find' implies a safe read operation, and the description adds valuable access context beyond what structured fields 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 appropriately sized and front-loaded. The purpose is stated in the first sentence, followed by a short clarification of output, a use-case sentence, and a structured Args list. Every sentence contributes value, and the list format improves scannability.
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 four optional parameters, an output schema, and a non-trivial cross-referencing behavior, the description covers purpose, usage, parameter details, and access requirements. It lacks explicit mention of return value structure, but the output schema is expected to fulfill that. The description is sufficiently complete for an agent to select and invoke it 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 input schema provides only types and defaults, with zero description coverage. The description's Args section fully explains each parameter: state (2-letter code, optional), days (lookback default 90), limit (max results default 15, max 50), and api_key (Starter tier required). This adds complete semantic meaning absent from 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 opens with a specific verb 'Find' and a clear resource: 'available talent from recent layoffs, cross-referenced with LCA visa roles.' This distinguishes it from sibling tools like get_recent_layoffs or get_company_layoffs by focusing on talent discovery and the unique LCA cross-reference. The purpose is 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 states it is 'Useful for recruiters targeting skilled workers from recently laid-off companies,' which gives a clear use case. It also mentions the Starter tier requirement as a prerequisite. However, it does not explicitly name alternative tools or when not to use it, leaving some room for interpretation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_layoffsAInspectSearch WARN Act layoff notices by company name, city, or keyword.
Args:
query: Search term (company name, city, etc.)
state: Optional 2-letter state code to filter (e.g. CA, TX, NY)
limit: Max results to return (default 20, max 100)
api_key: Optional API key for higher rate limits
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| state | No | ||
| api_key | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It usefully discloses that an API key can raise rate limits and that limit has default/max bounds. However, it does not mention whether results are paginated, whether authentication is required, or any side effects (though 'search' implies non-destructive).
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 compact and well-structured: a one-sentence purpose followed by a bullet-style Args list. Every line adds value and there is no filler.
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 covers input semantics and includes practical constraints (default/max, examples, API key purpose). An output schema exists, so return values are documented elsewhere. It lacks only an explicit tie to sibling tools and authentication prerequisites, which would make it fully self-sufficient.
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 no descriptions for any parameters (0% coverage), but the description's Args list fully explains all four parameters: query (search term), state (2-letter filter), limit (default/max), and api_key (rate limits). This directly compensates for the schema 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?
The description opens with a specific verb and resource: 'Search WARN Act layoff notices by company name, city, or keyword.' This clearly defines what the tool does and differentiates it from siblings like get_company_layoffs (targeted company lookup) and get_recent_layoffs (time-based listing).
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 conveys clear context for use: it searches notices by free-text terms with optional state and limit filters. However, it does not explicitly contrast with sibling tools or state when not to use it, such as when querying a single known company (for which get_company_layoffs might be better).
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.
No comments yet. Be the first to start the discussion!
