Structured company & industry news for AI agents: typed, dated, source-linked events.
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Average 3.5/5 across 4 of 4 tools scored. Lowest: 2.5/5.
Each tool has a distinct role: location_groups_list for hierarchy lookup, register_and_get_key_create for API access, and two stories endpoints differentiated by filter (industry/location vs organization). The two stories endpoints could be confused but their parameters clearly separate them.
Naming patterns are inconsistent: location_groups_list uses an object-first structure, stories_* endpoints are compound noun phrases, and register_and_get_key_create is a long multi-verb phrase. This mixing makes the set feel unpatterned.
With 4 tools, the server is lean but appropriately scoped for a focused news/stories API. It provides core query, location reference, and registration without unnecessary bloat.
The two story endpoints cover major lookup needs (by industry/location and organization), and location groups support filtering, but missing features like story detail retrieval, full-text search, or broader filtering (e.g., date/activity type) leave notable gaps.
location_groups_listCInspectLocation Groups are a way of grouping GeoNamesLocation entities into a simplified hierarchy.
They start from top-level regions using the United Nations M49 standard (Region/Sub-Region/Intermediate Region). The US is further broken down United States Census Bureau regions (e.g. East/West etc) and then into individual states. Certain other countries are also broken down into their states/provinces: AE, CA, CN, IN.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | A page number within the paginated result set. | |
| page_size | No | Number of results to return per page. |
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 does not state whether this operation is read-only, whether pagination is supported, what the response format is, or any side effects. The description is entirely about the entity, not the tool's 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 a multi-sentence paragraph explaining the data model. While not overly long, it spends time on context that is not directly tied to the tool's action. The structure is acceptable but not tightly focused on listing behavior.
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 lack of annotations and output schema, the description should explain what the tool returns, how pagination works, or any constraints. It only describes the concept behind the data, leaving a significant gap in understanding the tool's actual invocation and response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the two parameters (page, page_size), so the schema fully documents them. The description adds no additional meaning to the parameters, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explains what Location Groups are conceptually but does not explicitly state that the tool lists them. It lacks a clear verb like 'list' or 'get', and although the tool name implies the action, the description focuses on the data hierarchy rather than the operation.
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 guidance on when to use this tool vs. the sibling tools. It does not mention alternatives, conditions for use, or any exclusion criteria. The description only provides background on the data model, not usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
register_and_get_key_createAInspectCreates a user account with the given email, sends a verification email, and returns an API token. Unverified users are limited to 30 queries/month; verified users can make 300/month for free. Drop us an email to tell us how you found the service and if you want more queries (see website footer)
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | User email address |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It discloses that an account is created, a verification email is sent, and an API token is returned. It also provides rate limit context (30 vs 300 queries/month), which is useful behavioral information. Minor gaps exist (e.g., duplicate email behavior), but overall it is quite 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 three sentences and front-loaded with the core action. The final sentence about emailing for more queries is slightly tangential but still relevant to quota context. It is concise without being overly terse.
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 param, no output schema), the description covers the main workflow: account creation, verification email, token return, and quota limits. It lacks failure-mode details but is otherwise complete enough for an agent to understand the tool's role.
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% for the single email parameter, and the schema already describes it as 'User email address'. The description adds no extra semantic detail beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action: 'Creates a user account with the given email, sends a verification email, and returns an API token.' This is a specific verb+resource+outcome and clearly distinguishes from sibling list tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for initial registration and obtaining a token, but it does not explicitly state when to use it versus alternatives or provide exclusions. It does mention quota implications for unverified vs verified users, but that's more about post-registration behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
stories_industry_location_listAInspectStories by Industry/Location Simplified endpoint that runs the same logic as the main stories endpoint, with a restricted parameter set (only industry and location). Stories can either be
-- IndustrySectorUpdate
or activities in an organization's lifecycle. We track the following activities:
-- CorporateFinanceActivity: M&A, investments, stock purchases
-- PartnershipActivity: Partnership between 2 or more organizations. Often, companies describe their customers as their partners. This type of activity covers both genuine partnerships and customer/supplier relationships
-- RoleActivity: Key change in senior personnel, e.g. replacing CEO
-- LocationActivity: Opening or closing a new location (e.g. setting up in EMEA or shutting down a factory in a particular town)
-- ProductActivity: New product launches
-- AnalystRatingActivity: Updates from industry analysts
-- EquityActionsActivity: Stock repurchases, dividends etc
-- FinancialReportingActivity: Notice that an organization is going to announce its financials
-- FinancialsActivity: Information about company financials, e.g. revenue or EBITDA
-- IncidentActivity: Adverse incidents e.g. safety
-- LegalActivity: Lawsuits or activities that could lead to lawsuits, e.g. SEC investigations
-- MarketingActivity: e.g. launching a new advertising campaign
-- OperationsActivity: Company operations news, e.g. we are investing in a new product, or we have just completed an security audit
-- RecognitionActivity: e.g. we are delighted to announce that we won Agency of the Year
-- RegulatoryActivity: Legal or regulatory activity affecting this organizationg e.g. permit for drilling, or regulatory filing
Parameters:
include_provenance: Add a source_audit field to each story explaining how it was surfaced (source path, Typesense match score, and the entity that carried it). Defaults to false.
industry: Industry name to filter by. Accepts multiple values.
industry_context: Optional context aspects to anchor the industry search in. Pass each aspect as a separate value (e.g. "Packaging" when searching for "Rigid Metal"). Applied to every value of industry in the same request.
location: Location group name or id (e.g. "texas", "india", "MidWest", "FR", "us-ca"). Not case sensitive. Accepts multiple values. They must each match an id or name from the location groups endpoint.
output: Output format. Defaults to "simple" (SimpleStory schema). Use "full" for the detailed response.
page: A page number within the paginated result set.
page_size: Number of results to return per page.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | A page number within the paginated result set. | |
| output | No | Output format. Defaults to "simple" (SimpleStory schema). Use "full" for the detailed response. | |
| industry | No | Industry name to filter by. Accepts multiple values. | |
| location | No | Location group name or id (e.g. "texas", "india", "MidWest", "FR", "us-ca"). Not case sensitive. Accepts multiple values. They must each match an id or name from the [location groups](#/location-groups/location-groups_list) endpoint. | |
| page_size | No | Number of results to return per page. | |
| industry_context | No | Optional context aspects to anchor the industry search in. Pass each aspect as a separate value (e.g. "Packaging" when searching for "Rigid Metal"). Applied to every value of `industry` in the same request. | |
| include_provenance | No | Add a `source_audit` field to each story explaining how it was surfaced (source path, Typesense match score, and the entity that carried it). Defaults to false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It adds context about story types (e.g., CorporateFinanceActivity, RoleActivity) and the include_provenance behavior, but does not explicitly state read-only nature, rate limits, or pagination defaults. It's adequate but not rich.
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 lengthy due to an extensive enum-like list of activity types. While informative, it makes the description less concise and front-loaded. The redundant parameter listing adds bulk without new 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?
The description provides strong domain context (activity categories) and explains the simplified nature, but lacks explicit pagination defaults or response structure disclosure. However, with 100% schema coverage and no output schema, it's fairly complete for a simple listing 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?
Schema descriptions already cover all 7 parameters with examples and even a link to location groups. The description's parameter section duplicates the schema without adding new semantics, so it meets the baseline but doesn't add value 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?
The description explicitly states it is a 'Simplified endpoint' that retrieves stories filtered by industry and location, and distinguishes it from the main stories endpoint via restricted parameters. This clearly identifies the tool's scope and differentiates from sibling tools like stories_organization_list.
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 phrase 'runs the same logic as the main stories endpoint, with a restricted parameter set' provides an explicit contrast and implies when to prefer the full endpoint. It also references the location groups endpoint for valid location values, giving the agent integration guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
stories_organization_listAInspectStories by Organization Simplified endpoint that runs the same logic as the main stories endpoint, with a restricted parameter set (only org_name). Stories can either be
-- IndustrySectorUpdate
or activities in an organization's lifecycle. We track the following activities:
-- CorporateFinanceActivity: M&A, investments, stock purchases
-- PartnershipActivity: Partnership between 2 or more organizations. Often, companies describe their customers as their partners. This type of activity covers both genuine partnerships and customer/supplier relationships
-- RoleActivity: Key change in senior personnel, e.g. replacing CEO
-- LocationActivity: Opening or closing a new location (e.g. setting up in EMEA or shutting down a factory in a particular town)
-- ProductActivity: New product launches
-- AnalystRatingActivity: Updates from industry analysts
-- EquityActionsActivity: Stock repurchases, dividends etc
-- FinancialReportingActivity: Notice that an organization is going to announce its financials
-- FinancialsActivity: Information about company financials, e.g. revenue or EBITDA
-- IncidentActivity: Adverse incidents e.g. safety
-- LegalActivity: Lawsuits or activities that could lead to lawsuits, e.g. SEC investigations
-- MarketingActivity: e.g. launching a new advertising campaign
-- OperationsActivity: Company operations news, e.g. we are investing in a new product, or we have just completed an security audit
-- RecognitionActivity: e.g. we are delighted to announce that we won Agency of the Year
-- RegulatoryActivity: Legal or regulatory activity affecting this organizationg e.g. permit for drilling, or regulatory filing
Parameters:
org_name: Filter by organization name (partial match). (required)
output: Output format. Defaults to "simple" (SimpleStory schema). Use "full" for the detailed response.
page: A page number within the paginated result set.
page_size: Number of results to return per page.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | A page number within the paginated result set. | |
| output | No | Output format. Defaults to "simple" (SimpleStory schema). Use "full" for the detailed response. | |
| org_name | Yes | Filter by organization name (partial match). | |
| page_size | No | Number of results to return per page. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden, and it provides substantial context: it enumerates the specific activity types tracked and notes the simplified parameter set. It does not disclose read-only behavior, pagination details, or return structure beyond naming output schemas, but the activity list is a strong addition.
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 purpose, but the long enumeration of activity types, while informative, is verbose. The parameter section duplicates the schema, adding redundancy. It is structured but not as concise as it could be.
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 no output schema, the description does not fully explain the return value shape, only naming simple/full schemas. It covers activity categories and pagination parameters, but omits response structure and error behavior, leaving some gaps for an agent.
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 the description repeats the parameter descriptions already present in the schema. It adds no new parameter meaning; the activity type list is useful output context but not parameter semantics. Baseline 3 is appropriate given the schema covers all parameters.
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 this is a simplified stories endpoint filtered by organization, and distinguishes it from the main stories endpoint by restricting parameters to org_name. However, it does not explicitly differentiate from the sibling tool stories_industry_location_list, so it falls short of a 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 conveys when to use this endpoint: when querying stories by organization with a restricted parameter set. It does not explicitly mention alternatives or exclusions, but the context is clear enough for a specialized sub-endpoint.
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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"$schema": "https://glama.ai/mcp/schemas/connector.json",
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