Global flood events and extent 1985-present from the Dartmouth Flood Observatory and GFD.
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 4/5 across 5 of 5 tools scored.
Each tool has a clearly distinct purpose: discovery (get_catalog), metadata retrieval (get_pack), tool guidance (get_tool_help), data querying (query_dataset), and link search (search_disaster_links). No overlapping responsibilities; an agent can easily select the correct tool.
All tool names follow a consistent verb_noun snake_case pattern: get_catalog, get_pack, get_tool_help, query_dataset, search_disaster_links. The naming is uniform and predictable.
Five tools is well within the ideal range for a focused data-access server. Each tool earns its place, covering discovery, metadata, help, querying, and link search without bloat or deficiency.
The tool surface covers the full workflow: discover available packs (get_catalog), understand pack details and limits (get_pack), learn how to use tools (get_tool_help), query data (query_dataset), and explore cross-disaster links (search_disaster_links). No critical gaps are evident.
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 '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.
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?
Annotations already declare readOnlyHint=true. Description adds context that it's a 'Free discovery' call and lists what it returns (metadata, coverage, etc.). No contradictions. Adequate behavioral transparency for a read-only metadata tool.
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, no unnecessary words. Front-loaded with key outcome ('Free discovery') and actionable guidance. Every sentence 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?
Given simple complexity (one parameter, no output schema), the description fully covers what the tool does, what it returns, and when to use it. No gaps.
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 parameter description listing possible pack_id values. Tool description does not add additional semantic meaning beyond the schema, but it reinforces that the parameter refers to a single pack. 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?
Description clearly states 'Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack.' It specifies the resource (pack) and the action (get), and distinguishes from siblings like query_dataset by framing it as a pre-query discovery tool.
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 'Call this before querying a new pack' and explains benefits like seeing time shape and coverage limits. Provides clear context for when to use, though does not explicitly mention when not to use or alternatives besides the implied distinction from query_dataset.
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?
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.
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?
The readOnlyHint annotation already covers safety. The description adds valuable context by distinguishing free vs. paid packs and mentioning the API contract, but it doesn't disclose response format, pagination, or other behavioral quirks 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?
The description is compact and well-structured: the core purpose and API contract are front-loaded, followed by a concise list of available packs. Every sentence serves a purpose without redundant content.
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 8 parameters and no output schema, the description provides a high-level contract reference and pack availability but lacks detail on response shape or edge cases. Given the schema's thoroughness and read-only annotation, it is adequate but not 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?
Schema coverage is 100%, so the baseline is 3. The description enriches parameter selection by listing free and paid packs with pricing, which is not present in the schema's pack_id description. This helps agents choose appropriate values beyond the schema examples.
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 identifies query_dataset as a structured query tool for direct data access via source_id or pack_id, using a specific API contract. It distinguishes itself from metadata-based siblings like get_catalog and get_pack, though it doesn't explicitly state the return type (e.g., data rows).
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 querying the listed packs/sources but does not explicitly contrast with sibling tools or state when not to use it. No clear alternative guidance is given, leaving selection partially inferred.
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 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.
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
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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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
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