US tornado events 1950-present from the NOAA Storm Prediction Center: EF rating, tracks, damage.
Free 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.
Free 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 from get_catalog. Newly catalog-admitted packs require no MCP schema change. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already set readOnlyHint=true, and the description adds 'Free discovery' and explains what information is returned. It does not contradict annotations. It provides useful behavioral context (time shape, coverage limits, paste-ready query) beyond the annotation, enriching the agent's understanding of the tool's output and role.
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 tight sentences with information front-loaded: first states return value, second gives usage guidance. No filler words, and every clause 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, read-only tool with no output schema, the description covers what the tool returns, when to use it, and why it matters. It includes specifics like 'time shape, coverage limits, and the paste-ready first query' which gives an agent everything needed to invoke it appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with pack_id described as 'Pack identifier from get_catalog'. The description adds no additional parameter details beyond the schema, so the baseline 3 applies.
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 ('Returns') and resource ('detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack'). It clearly distinguishes from get_catalog by focusing on a single pack and positioning it as a discovery step before querying, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Call this before querying a new pack', which gives a concrete use condition. It also hints at when it is not needed (when you already know the pack details). It does not name alternative tools explicitly, but the context makes it clear this is a pre-query discovery step.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free 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.
Generic 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 from get_catalog. Newly catalog-admitted packs require no MCP schema change. | |
| 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 indicate readOnlyHint=true, and the description aligns with that by describing a query operation. It adds valuable behavioral context beyond annotations by naming free vs. paid packs and mentioning the x402 Base USDC payment requirement, which an agent would otherwise not know.
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 core purpose is stated early, followed by a concise enumeration of packs and pricing. It is information-dense but not bloated; every clause contributes useful selection or cost context.
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 references an external API contract and lists packs, but with no output schema and many flexible parameters (filters, metrics, output), an agent might need more clarity on response shape or how this relates to get_catalog for discovering pack_ids. It is adequate but leaves some operational context to inference.
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 the baseline is 3, but the description adds significant value by enumerating actual pack names and differentiating free vs. paid packs. This directly helps agents choose valid pack_id or source_id values beyond the generic 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?
Description clearly states this is a structured query tool for direct source_id or pack_id access, which distinguishes it from catalog/help/search siblings. It uses a specific verb ('query') and resource, though it could more explicitly contrast with get_catalog and get_pack.
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 when to use the tool: querying data from known packs or source ids. It provides a helpful list of available packs, but it does not explicitly state when to prefer this over sibling tools or 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.
Free 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_pack1 field changedInput schema / properties / pack_id / descriptionPrevious value: -"Pack identifier such as 'currency', 'earthquakes', 'floods', 'hurricanes', 'tornadoes', 'tsunamis', 'un_sdg', 'volcanoes', 'world_factbook', or 'worldpop'."New value: +"Pack identifier from get_catalog. Newly catalog-admitted packs require no MCP schema change."query_dataset1 field changedInput schema / properties / pack_id / descriptionPrevious value: -"Pack identifier such as 'currency', 'earthquakes', 'floods', 'hurricanes', 'tornadoes', 'tsunamis', 'un_sdg', 'volcanoes', 'world_factbook', or 'worldpop'."New value: +"Pack identifier from get_catalog. Newly catalog-admitted packs require no MCP schema change."get_tool_helpsearch_disaster_linksget_pack1 field changedInput schema / properties / pack_id / descriptionPrevious value: -"Pack identifier such as 'currency', 'earthquakes', 'volcanoes', 'tsunamis', 'hurricanes', 'un_sdg', 'world_factbook', or 'worldpop'."New value: +"Pack identifier such as 'currency', 'earthquakes', 'floods', 'hurricanes', 'tornadoes', 'tsunamis', 'un_sdg', 'volcanoes', 'world_factbook', or 'worldpop'."query_dataset1 field changedInput schema / properties / pack_id / descriptionPrevious value: -"Pack id such as 'currency', 'earthquakes', 'volcanoes', 'tsunamis', 'hurricanes', 'un_sdg', 'world_factbook', or 'worldpop'."New value: +"Pack identifier such as 'currency', 'earthquakes', 'floods', 'hurricanes', 'tornadoes', 'tsunamis', 'un_sdg', 'volcanoes', 'world_factbook', or 'worldpop'."get_catalogget_packquery_datasetClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Global hurricane and tropical cyclone tracks from IBTrACS/NOAA, 1842-present. Wind and pressure.
Global earthquake events from the USGS, 2150 BC-present: magnitude, depth, location, counts.
Global tsunami events from NOAA NCEI, 2000 BC-present: wave height, runups, and counts.
NOAA Storm Prediction Center — outlooks, storm reports, watches
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: catalog discovery, pack metadata retrieval, tool guidance, dataset querying, and cross-disaster link search. There is no overlap or ambiguity that would cause an agent to misselect a tool.
All tool names follow a consistent verb_noun snake_case pattern (get_catalog, get_pack, get_tool_help, query_dataset, search_disaster_links), with clear prefixes indicating action type. The naming is uniform and predictable.
Five tools is a well-scoped set for a data discovery and query server. Each tool earns its place, covering discovery, help, generic querying, and cross-event relationship search without unnecessary overlap or bloat.
The tool set covers the full lifecycle of data access: discovery (get_catalog, get_pack), guidance (get_tool_help), querying (query_dataset), and cross-disaster linkage (search_disaster_links). There are no obvious gaps that would hinder an agent from accomplishing its tasks.