Real-time corroborated news events + 5-year archive, for agents. Free tier, no key.
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 4.2/5 across 5 of 5 tools scored.
Each tool has a distinct purpose: corpus_stats for metadata, get_event for a single event, get_events for querying deduped events, list_sources for source info, and search_articles for raw articles. No overlap.
All names follow a consistent verb_noun pattern using snake_case (corpus_stats is a slight exception but still fits). The naming is predictable and clear.
Five tools cover the core needs of a news API: statistics, event retrieval, event querying, source listing, and article search. This is well-scoped and not excessive.
The tool set covers key operations for a read-only news aggregator. Minor gaps exist, such as a dedicated tool to fetch a single article by ID, but search_articles can likely serve that purpose.
corpus_statsACorpus size and freshness: source / article / event counts and the latest article timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds that the tool returns counts and a timestamp, which aligns with annotations. No additional behavioral details (e.g., caching, empty corpus handling) are provided, but annotations cover the safety profile adequately.
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 concise sentence that front-loads the purpose and key outputs. Every word is meaningful with no 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?
No output schema exists, but the description lists the main output elements (counts and latest timestamp). Minor details like timestamp format or behavior for empty corpus are omitted, but for a simple stats tool, this is reasonably complete.
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?
There are no parameters (schema coverage 100% with empty properties). Baseline for 0 parameters is 4. The description does not need to explain parameters, and is clear about what the tool returns.
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 corpus size and freshness metrics (source/article/event counts and latest article timestamp). This distinguishes it from sibling tools like get_event, get_events, list_sources, and search_articles, which operate on specific items rather than aggregate stats.
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 obtaining overall corpus statistics, but does not explicitly state when to use this tool versus alternatives (e.g., for detailed event queries, use get_events). No guidance on 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_eventAFetch a single event by id, including every corroborating article (source, url, timestamp).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Event id from get_events |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds behavioral context about the return structure (includes corroborating articles with source, url, timestamp), which goes 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, efficient sentence with no wasted words. Every part adds 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?
For a single-parameter retrieval tool, the description is complete: it specifies the input (id), output content (corroborating articles details), and safety (annotations). No output schema, but the description covers the return structure adequately.
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 parameter 'id' is described as 'Event id from get_events'. The description does not add any additional meaning to the parameter beyond what the schema provides; it only describes the return format.
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 'Fetch' and the resource 'a single event', specifying that it includes every corroborating article with source, url, and timestamp. This distinctly distinguishes it from sibling tools like get_events (list) or search_articles.
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 when you need a specific event by id, but does not explicitly state when to use alternatives like get_events for multiple events or search_articles. No exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_eventsAQuery the deduped news event graph by theme, source, category and time window. Returns events (many articles about one happening collapsed into one), each with a corroboration count and confidence score. This is the primary tool — prefer it over raw articles. The corpus spans 260+ global outlets and a five-year archive (history visible depends on tier).
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Free-text theme/keyword to match on headline + summary | |
| to | No | Only events active on/before this date. ISO 8601 date/time, e.g. 2026-07-01 or 2026-07-01T00:00:00Z | |
| from | No | Only events active on/after this date. ISO 8601 date/time, e.g. 2026-07-01 or 2026-07-01T00:00:00Z | |
| limit | No | Max events (default 25) | |
| source | No | Source slug, e.g. 'coindesk' (see list_sources) | |
| category | No | Source category | |
| semantic | No | Rank by meaning (embedding similarity) instead of keyword match — requires q; better for concepts ('monetary easing') than exact strings |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that events are deduped, have corroboration count and confidence score, and the corpus spans 260+ outlets with a five-year archive. 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 cover purpose, output characteristics, and corpus scope. The first sentence fronts the main purpose, making it easy for the agent to grasp quickly. 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?
Given no output schema, the description explains return structure (events with corroboration count and confidence score). It covers the basic purpose, filtering dimensions, and usage guidance. Missing explicit pagination info, but limit parameter is described in schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description mentions filtering by theme, source, category, and time window, which aligns with parameters but adds no significant new semantic detail beyond the schema's own 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 queries a deduped news event graph by theme, source, category, and time window. It distinguishes itself from raw articles by noting it collapses many articles into one event and that it is the primary tool to prefer.
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 states 'This is the primary tool — prefer it over raw articles,' which guides the agent to choose this over search_articles. It does not provide explicit when-not-to-use scenarios, but the preference is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_sourcesAList all news sources Newsflash tracks, with categories and live article counts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds that it returns categories and article counts, which is useful. It does not mention pagination or size limits, but with no parameters, this is minor.
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?
Single sentence, no fluff, front-loaded with key information. Every word is meaningful.
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?
Description explains what is returned (categories, article counts) but could mention that the list is complete and unfiltered. For a simple list tool without output schema, it is largely 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?
No parameters exist, and schema coverage is 100%. The description correctly adds no parameter-level details. Baseline 4 is appropriate for zero-parameter tools.
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 the tool lists all news sources with categories and live article counts. It is distinct from sibling tools like search_articles (search) and get_events (events).
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 for retrieving a full list of sources, but does not explicitly specify when not to use it or compare to alternatives. Given the simplicity, the purpose is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_articlesASearch raw articles (not collapsed into events). Use when you want the underlying items or need per-outlet coverage rather than deduped happenings.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Free-text keyword on headline + summary | |
| to | No | ISO 8601 date/time, e.g. 2026-07-01 or 2026-07-01T00:00:00Z | |
| from | No | ISO 8601 date/time, e.g. 2026-07-01 or 2026-07-01T00:00:00Z | |
| limit | No | Max articles (default 25) | |
| source | No | Source slug | |
| category | No | Source category |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context about returning raw, per-outlet articles, which aligns with annotations and provides additional behavioral insight. No contradictions.
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, front-loaded with the purpose, and contains no unnecessary 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?
The tool has 6 parameters with full schema descriptions, but there is no output schema or return value description. Given that it's a search tool with read-only intent, the description is nearly complete, but a brief note on response structure or examples would improve completeness.
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?
All six parameters have descriptions in the schema (100% coverage). The tool description does not add any additional parameter information beyond what the schema already provides, so it adds no extra value for parameter 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 'Search raw articles' and distinguishes from events by explicitly saying 'not collapsed into events' and contrasting with 'deduped happenings', which differentiates it from sibling tools like get_events.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Use when you want the underlying items or need per-outlet coverage rather than deduped happenings.' This clearly indicates the context and alternatives (sibling tools that return collapsed events).
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!
Sign in to create a connector for this server.