Global flood events and extent 1985-present from the Dartmouth Flood Observatory and GFD.
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 'Free discovery' beyond the readOnlyHint annotation, but does not detail other behavioral traits such as response format or limitations. Since annotations already indicate read-only, the description provides marginal additional context.
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 extremely concise at two sentences, with no filler. Every word serves a purpose, and the key information is front-loaded.
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 adequately explains the return value (list of packs). With zero parameters and clear sibling differentiation, the tool is well-specified for its simplicity.
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, so schema coverage is trivially 100%. The description adds no parameter-specific meaning, which is acceptable for a parameterless tool. Baseline score of 4 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 clearly states it returns a list of live agent-ready data packs on DaedalMap, using a specific verb and resource. It distinguishes itself from siblings like get_pack (which likely returns a specific 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 phrase 'Free discovery' implies it is for exploring available packs, but there is no explicit guidance on when to use this tool versus alternatives like get_pack. Usage context is implied rather than clearly stated.
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 declare readOnlyHint=true, and the description reinforces this with 'Free discovery' while adding context about what the returned metadata includes and the recommended pre-query behavior. No contradictions; it adds useful behavioral context beyond 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?
Two sentences with no filler: the first states what is returned, the second states when to call it. The most decision-relevant information (discovery, metadata, first-query examples) is front-loaded.
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 read-only, single-parameter lookup tool with no output schema, the description covers what it returns, why it exists, and when to call it. The only missing piece (pack listing) is handled by the get_catalog sibling referenced in the 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 the schema fully documents pack_id as a string identifier from get_catalog. The description adds no new parameter-level detail, which is acceptable under the baseline-3 rule for complete schema coverage.
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 ('Returns detailed metadata... for one pack') and enumerates the kinds of information returned (coverage, freshness, canonical tool guidance, first-query examples). It distinguishes this tool from siblings: get_catalog supplies the pack listing, while get_pack is the per-pack discovery step.
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 when-to-use guidance: 'Call this before querying a new pack so you can see time shape, coverage limits, and the paste-ready first query.' It does not explicitly name exclusion conditions or contrast with get_catalog/get_tool_help, but the intended call sequence is clear.
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?
Annotations already declare readOnlyHint=true, and the description does not contradict this. It adds clarifying context about the tool's nature ('Free blind-caller guidance') and enumerates the behavioral/return categories (refusals, access limits, provenance fields, recommended next calls), which helps the agent predict what to expect beyond a simple read.
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 just two sentences, front-loads the core purpose, and packs the second sentence with a structured list of return contents. Every phrase earns its place, and the length is appropriate for the tool's informational 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?
With no output schema, the description compensates by enumerating the major output categories (when to use it, refusals, example, access limits, outputs, provenance, next calls, interaction contract). It also covers the discovery workflow, making the tool fully self-contained for an agent encountering 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?
The schema already fully describes the only parameter ('tool_name' as 'Exact tool name from tools/list'), so schema coverage is 100%. The description reinforces this by mentioning 'Use tools/list to discover names,' but adds no new parameter-level detail that the schema doesn't already provide.
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 action ('Returns guidance for one tool') and names the resource ('one tool visible on this MCP facade'). It clearly distinguishes itself from sibling data tools by being a meta-tool and even points to tools/list for discovery.
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?
Explicit usage guidance is provided: 'Use tools/list to discover names, then call this before an unfamiliar tool.' This gives a concrete workflow and positions the tool as a prerequisite before using unfamiliar tools, making when-to-use 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 declare readOnlyHint=true, and the description adds useful behavior beyond that: the endpoint contract, the free/paid distinction, and the x402 Base USDC cost for paid packs. It does not disclose output format or pagination, but the read-only annotation lowers the burden, so this is solid coverage.
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 first sentence states the core purpose, and the subsequent pack lists are compact and directly useful for selecting valid identifiers. The enumeration of packs and costs is slightly long but earns its place since there is no enum in the schema.
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 generic query tool with 8 optional parameters, no required fields, and no output schema, the description should explain that at least one of source_id or pack_id is expected and what the response shape looks like. It relies on the external endpoint contract for that, and while the free/paid pack context is helpful, the missing operational prerequisites leave an agent to guess.
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 schema already documents each parameter. The description adds value by listing concrete pack IDs, giving example source_id values, and noting cost, which meaningfully enriches the otherwise 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?
The description opens with a specific verb and resource: 'Generic structured query for direct source_id or pack_id access.' It names the two query targets and ties the behavior to a concrete API contract, distinguishing it from catalog/discovery tools like get_catalog.
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 clearly implies the tool is for querying known source IDs or pack IDs and enumerates which packs are free vs paid. It does not explicitly state when not to use it or mention alternatives like get_catalog or search_disaster_links, so it falls just short of full usage guidance.
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 provide readOnlyHint=true, so the description adds limited behavioral context. It describes the tool as a 'discovery helper' and mentions optional filtering, but doesn't elaborate on rate limits, authentication, or return behavior beyond what is implied.
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 concise: two sentences covering the core functionality and usage context. It is front-loaded with the action verb 'Searches' and efficiently conveys the main point without 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?
Given the 7 parameters and no output schema, the description is somewhat minimal. It covers the primary use case but does not explain expected output format or how results are structured. For a search tool, this is adequate but not fully comprehensive.
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 7 parameters have descriptions in the schema (100% coverage), so the description does not add new semantic information. It reiterates the parameters already documented, such as 'event-type direction' and 'via-event type', but no additional detail is provided.
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: searching cross-disaster link families by event-type direction, via-event type, and year window. It distinguishes from siblings (get_catalog, get_pack, query_dataset) by emphasizing discovery before having an exact event id, making it distinct.
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.' This provides clear context for when to use the tool, though it does not mention when not to use it or alternatives.
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 tsunami events from NOAA NCEI, 2000 BC-present: wave height, runups, and counts.
Global volcanic eruptions from the Smithsonian Global Volcanism Program: VEI, location, dates.
Federal disaster declarations, emergencies, and assistance data
Global earthquake events from the USGS, 2150 BC-present: magnitude, depth, location, counts.
Add one secure layer between your agents and this server.
Each tool has a clear, distinct role: catalog discovery, pack metadata, tool help, data querying, and link searching. There is no overlap between any two tools; even get_catalog and get_pack differ as list vs. details.
All tool names follow a consistent verb_noun snake_case pattern: get_catalog, get_pack, get_tool_help, query_dataset, search_disaster_links. The verbs and nouns are consistently ordered, making the API predictable.
Five tools is well-scoped for a data access facade. Each tool earns its place: discovery (catalog, pack, help), execution (query), and specialized search (links). No redundancy or bloat.
The server covers the full lifecycle of data exploration: discover available packs, inspect pack details, understand tool usage, execute queries, and search for relationships. There are no apparent dead ends—an agent can go from discovery to successful query.