US tornado events 1950-present from the NOAA Storm Prediction Center: EF rating, tracks, damage.
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 role: catalog listing, pack metadata, tool guidance, dataset querying, and link discovery. There is no overlap in their purposes.
All tool names follow a clear verb_noun snake_case pattern (get_*, query_*, search_*) which is consistent and predictable.
Five tools cover the discovery and querying workflow without unnecessary bulk. The count is well-scoped for a data access server.
The tool surface supports the full discovery-to-query journey: list packs, inspect pack details, get tool help, query datasets, and search linked disaster data. No obvious gaps exist for the stated purpose.
get_catalogGet CatalogAFree discovery. Returns the list of live agent-ready data packs available on DaedalMap.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond the readOnlyHint annotation by emphasizing 'Free discovery' and 'live agent-ready data packs', which clarifies the nature of the data returned. It does not mention any side effects, auth requirements, or rate limits, but given the read-only annotation and zero parameters, the behavior is transparent enough.
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, front-loaded sentence that immediately conveys the tool's purpose. Every word adds value, and there is no redundancy or filler. It is an excellent model of 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?
Given the tool's simplicity (no params, read-only, no output schema), the description provides a clear picture of what the tool returns and on what platform. It does not detail the structure of the returned list, but for a catalog discovery tool, the description is adequate and complete enough for an agent to select and invoke it.
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?
With no parameters and 100% schema coverage (vacuously), the description does not need to explain parameters. The baseline for zero parameters is 4, and the description appropriately focuses on the tool's output rather than input 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 the tool's function: it returns a list of live agent-ready data packs on DaedalMap. The verb 'returns' and the specific resource ('data packs') make the purpose explicit. It is distinct from siblings like get_pack (retrieving a specific pack) and query_dataset (querying data).
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 intended use is implied: it is a discovery tool to list available data packs. However, the description does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or prerequisites. With siblings like get_pack, explicit guidance would improve clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_packGet PackAFree discovery. Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack. Call this before querying a new pack so you can see time shape, coverage limits, and the paste-ready first query.
| Name | Required | Description | Default |
|---|---|---|---|
| pack_id | Yes | Pack identifier such as 'currency', 'earthquakes', 'floods', 'hurricanes', 'tornadoes', 'tsunamis', 'un_sdg', 'volcanoes', 'world_factbook', or 'worldpop'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint already covers the read-only safety profile. The description adds useful context about the return content (metadata, coverage, freshness, examples) and the 'free discovery' nature, but it does not disclose additional behavioral traits such as rate limits, pagination, or error conditions. Since annotations cover safety, a 3 is appropriate.
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 'Free discovery,' and every clause serves a purpose: it states what the tool returns and when to use it. No fluff or repetition of schema details.
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 simple tool with one parameter and no output schema, the description adequately explains the return values (metadata, coverage, freshness, first-query examples) and provides the recommended usage context. It is complete enough for an agent to know how 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 schema fully describes the single parameter pack_id with examples (100% schema coverage). The description mentions 'one pack' but adds no additional parameter semantics beyond what the schema already provides. Baseline 3 is correct.
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 'detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack,' which is a specific verb-resource pair. It is distinguishable from siblings like get_catalog (list packs) and query_dataset (query data) because it focuses on a single pack's metadata.
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 says 'Call this before querying a new pack,' giving a clear when-to-use scenario. It does not explicitly name alternatives or when-not-to-use, but the context implies this is a prerequisite step before using query_dataset, which is sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_tool_helpGet Tool HelpAFree blind-caller guidance for one tool visible on this MCP facade. Returns when to use it, what it refuses, a working example, effective access limits, important outputs, provenance fields, recommended next calls, and the shared natural-language-to-strict-JSON interaction contract. Use tools/list to discover names, then call this before an unfamiliar tool.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_name | Yes | Exact tool name from tools/list. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already declares this as a safe read operation. The description adds valuable behavioral context by detailing what the tool returns, including refusals, working examples, access limits, and provenance fields. No contradictions 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?
Two compact sentences: the first front-loads the core purpose and outputs, the second provides usage guidance. Every phrase contributes meaning, and there is no redundancy or 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?
For a single-parameter, read-only helper tool with no output schema, the description covers purpose, output contents, and usage workflow. It is self-contained and sufficient for an agent to 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 already provides full coverage for the single parameter ('Exact tool name from tools/list'). The description reinforces this by mentioning tools/list but adds no new semantic detail beyond the schema's 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 it provides 'blind-caller guidance for one tool' and enumerates the specific information returned (when to use, refusals, example, limits, outputs, etc.). This distinguishes it from sibling data-access tools like get_catalog or query_dataset.
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 instructs to 'Use tools/list to discover names, then call this before an unfamiliar tool.' This gives a clear when-to-use directive and the prerequisite step, making the usage context unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_datasetQuery DatasetAGeneric structured query for direct source_id or pack_id access using the same contract as POST /api/v1/query/dataset. Free packs: currency, distributed_manufacturing, floods, nri, owid, un_sdg, un_wpp, volcanoes, world_bank_wdi. Paid packs: earthquakes, hurricanes, tornadoes, tsunamis, wildfires, world_factbook, worldpop (x402 Base USDC).
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | Optional sort instructions for row-returning queries. | |
| limit | No | Maximum number of rows to return for the requested source or pack. | |
| output | No | Optional output controls such as response format hints. | |
| filters | No | Structured filters including time, region_ids, and compare clauses. | |
| metrics | No | Metric ids to return. Use event_count for aggregate counts when supported. | |
| pack_id | No | Pack identifier such as 'currency', 'earthquakes', 'floods', 'hurricanes', 'tornadoes', 'tsunamis', 'un_sdg', 'volcanoes', 'world_factbook', or 'worldpop'. | |
| source_id | No | Concrete source id such as 'earthquakes_events', 'volcanoes_events', 'hurricanes_events', or 'un_sdg/01'. | |
| request_id | No | Optional caller-supplied request id for tracing and idempotency. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as readOnly=true. The description adds meaningful context beyond the annotation by referencing the API contract (POST /api/v1/query/dataset), listing free vs. paid packs with pricing (x402 Base USDC), and clarifying direct access semantics. It does not contradict the annotation.
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 core purpose, and every word earns its place. The pack list is lengthy but necessary for selecting valid pack_id values. No fluff or 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?
For an 8-parameter tool with no output schema, the description provides good context: it defines the query contract, lists all valid packs with pricing, and clarifies the direct-access pattern. It does not describe the response shape or pagination behavior, but the schema's limit parameter and the read-only annotation cover some of that gap. Overall, it is sufficiently complete 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 input schema covers 100% of parameters with descriptions. The description adds some value by naming the two primary identifier parameters (source_id and pack_id) and providing the full pack list with pricing, which supplements the pack_id parameter. However, it does not deeply explain parameter interactions or formats beyond what the schema already provides.
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 purpose: 'Generic structured query for direct source_id or pack_id access.' It uses a specific verb (query) and resource (dataset), and distinguishes it from siblings by emphasizing direct ID-based access rather than catalog browsing or search.
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 clear context: use this tool when you have a specific source_id or pack_id to query data. It also lists free vs. paid packs, which helps the agent choose appropriate pack_id values. However, it does not explicitly mention alternatives like get_catalog for listing or search_disaster_links for searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_disaster_linksSearch Disaster LinksAFree linked-disaster discovery helper. Searches published cross-disaster link families by event-type direction, optional via-event type, and optional year window. Use this when you want to discover whether a relationship family exists before you have an exact event id.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of matching chains to return. Default 10. | |
| year_end | No | Optional inclusive ending year filter. | |
| request_id | No | Optional caller-supplied request id for tracing. | |
| year_start | No | Optional inclusive starting year filter. | |
| end_event_type | No | Optional ending event type such as tsunami, flood, tornado, or earthquake. | |
| via_event_type | No | Optional intermediate event type for bounded chain discovery. | |
| start_event_type | No | Optional starting event type such as earthquake, hurricane, volcano, wildfire, flood, tornado, or tsunami. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds context that the tool is for discovery before having an exact event ID, implying no ID requirement. It does not mention rate limits, pagination, or return format, but the annotation covers the main safety concern.
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 mostly efficient. The first sentence 'Free linked-disaster discovery helper' is somewhat filler, but the second and third sentences are substantive. No unnecessary detail.
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 7 optional parameters and no output schema, the description provides a clear purpose but does not explain how parameters combine (e.g., direction requires start and end?) or what the response looks like. It is adequate for initial understanding but leaves gaps for an agent deciding on parameter values.
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 descriptions for 100% of the parameters. The description mentions event-type direction, via-event type, and year window, which map to schema parameters, but adds no extra syntax, defaults, or relationships. Baseline 3 is appropriate since the schema does the heavy lifting.
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 searches published cross-disaster link families by event-type direction, optional via-event type, and year window. The verb 'searches' is specific, the resource is defined, and the usage context ('discover whether a relationship family exists before you have an exact event id') further clarifies its role.
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 says 'Use this when you want to discover whether a relationship family exists before you have an exact event id,' providing clear when-to-use guidance. However, it does not name alternatives or explicitly state when not to use it, so it falls short of the highest level.
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",
"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.
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