DaedalMap Distributed Manufacturing Locations
Server Details
Open manufacturing and maker facility locations for country and facility-type queries.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.8/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: get_catalog for listing packs, get_pack for detailed metadata, and query_dataset for executing queries. There is no overlap or ambiguity.
All tool names follow the consistent verb_noun pattern in snake_case (get_catalog, get_pack, query_dataset), making the interface predictable.
With 3 tools covering discovery, metadata retrieval, and querying, the count is well-scoped for the server's purpose. No tool seems redundant or missing.
The tool surface covers the full lifecycle for a data catalog service: listing available packs, getting detailed info, and querying data. There are no obvious gaps for the stated purpose.
Available Tools
3 toolsget_catalogGet CatalogARead-onlyInspect
Free discovery. Returns the list of live agent-ready data packs available on DaedalMap.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description adds minimal behavioral context beyond indicating a read operation. It adds some context about what is returned (live data packs) but no deeper behavioral traits.
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?
A single, front-loaded sentence that is concise and to the point. Every word adds value 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?
For a simple tool with no parameters and no output schema, the description sufficiently explains the return value (list of data packs) and purpose. It matches the tool's 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?
No parameters exist, so baseline is 4. The description does not need to add parameter semantics as there are none.
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 a list of live agent-ready data packs, effectively distinguishing it from sibling tools like get_pack and 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?
No explicit guidance on when to use this tool versus alternatives. The description only mentions 'Free discovery,' which implies initial exploration, but lacks when-not-to-use or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_packGet PackARead-onlyInspect
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 such as 'currency', 'earthquakes', 'floods', 'hurricanes', 'tornadoes', 'tsunamis', 'un_sdg', 'volcanoes', 'world_factbook', or 'worldpop'. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as readOnlyHint=true. The description adds useful behavioral context: it is a discovery, returns specific metadata, and is intended as a pre-query step. 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?
Two sentences: first describes what the tool returns, second gives usage guidance. No redundant words, information is front-loaded and clear.
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 metadata retrieval tool with one parameter and no output schema, the description covers the key aspects: what is returned, when to use it, and the benefit. It adequately informs an agent without needing extensive detail.
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% for the single parameter pack_id, which includes descriptive examples. The description does not add further detail to the parameter meaning beyond the schema, but provides usage context (e.g., 'one pack') that aligns with the parameter.
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 for one pack, with specific items like coverage, freshness, canonical tool guidance, and examples. It distinguishes itself from siblings by focusing on pre-query discovery rather than catalog listing (get_catalog) or data querying (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 advises calling this tool before querying a new pack to understand time shape and coverage limits. The context of siblings (get_catalog, query_dataset) implies alternatives, but no explicit when-not-to-use statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_datasetQuery DatasetBRead-onlyInspect
Generic structured query for direct source_id or pack_id access using the same contract as POST /api/v1/query/dataset. Free packs: boundaries, currency, distributed_manufacturing, floods, geography, nri, owid_co2, reverse-geocoding, 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, confirming the tool is safe for reads. The description adds context beyond annotations by specifying the API contract endpoint and listing supported free and paid packs. However, it does not disclose the return format, pagination behavior, or any rate limits, which would further aid transparency.
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 with no wasted words. The first sentence states the core purpose and contract, and the second lists packs. Information is front-loaded and efficient.
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 complexity (8 parameters, nested objects, no output schema), the description provides high-level purpose and pack lists but lacks details on return values, error handling, or pagination. The annotations supply readOnlyHint, but the description could be more complete for a tool of this complexity. It is adequate but not thorough.
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 all 8 parameters with descriptions, achieving 100% schema description coverage. The tool description adds no additional parameter-level information beyond what is already in the schema. Thus, it does not compensate further, and a baseline score of 3 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 identifies the tool as a generic structured query for source_id or pack_id access, referencing a specific API contract. It lists supported packs, distinguishing free from paid. However, it does not explicitly differentiate from sibling tools like get_catalog or get_pack, though the verb 'query' implies data retrieval versus metadata listing.
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?
No explicit guidance on when to use this tool versus alternatives such as get_catalog or get_pack. The description lists packs but does not include when-to-use or when-not-to-use scenarios, prerequisites, or exclusions. Users must infer usage from the tool's name and description.
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" }]
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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
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Credentials required to access the server are missing or invalid
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