List the data-source endpoints feeding the wire, with provider, signal type, country, cadence, and license. Returns one SRC line per source.
| Name | Required | Description | Default |
|---|---|---|---|
| signal_type | No | Filter by signal type, e.g. climate, security, health. Omit for all sources. |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already establish that the tool is read-only, idempotent, and closed-world. The description adds value beyond these annotations by specifying the output shape: 'Returns one SRC line per source,' and by listing the included fields, giving the agent a concrete expectation of the result despite the absence of an output schema.
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 short sentences with no filler. It front-loads the core action and resource, then follows with the output format and content. Every sentence contributes meaningful information.
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 low complexity (one optional parameter), strong safety annotations, and the description's coverage of purpose, output format, and fields, the definition is complete enough for an agent to invoke the tool correctly without further documentation.
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 has full (100%) parameter description coverage, so the baseline is 3. The schema already explains that signal_type filters by examples and can be omitted; the tool's description does not add any additional parameter semantics beyond what the schema 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 uses the specific verb 'List' and clearly identifies the resource as 'data-source endpoints feeding the wire,' enumerating the returned fields: provider, signal type, country, cadence, and license. It also states the output form, 'one SRC line per source,' which distinguishes it from sibling list tools like list_facets and list_fusion_products.
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 the appropriate use case: when an agent needs a catalog of data-source endpoints and their associated metadata. However, it does not explicitly mention when not to use this tool or name alternatives such as search_signals or get_signal, leaving some routing inference to the agent.
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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Each tool has a clearly distinct purpose: get_signal re-reads a known row, get_latest_signals fetches by time, scope_signals filters by facets, search_signals matches words, and get_related_signals follows links. The overlapping pairs like get_facet_manifest/list_facets and get_fused_signal/list_fusion_products are explicitly differentiated in their descriptions, so an agent should not confuse them.
The naming follows a consistent snake_case verb_noun pattern: get_ for direct fetches, list_ for catalog-style enumeration, register_ for identity creation, and scope_/search_ for query actions. The slight difference between get_fused_signal and list_fusion_products is meaningful and the verbs remain predictable.
Thirteen tools is well within the sweet spot and each one covers a distinct capability: live reads, lookup by id, lexical search, facet filtering, related signals, fused products, catalogues, plans, billing, and agent registration. There is no obvious padding or excessive fragmentation.
The surface fully covers the domain: discovering the vocabulary, selecting signals, searching, fetching by id, following relationships, computing derived products, listing sources, and checking billing/plans. The only gaps would be account claiming and credential rotation, but those are explicitly deferred to external parties, so they are not tool-set gaps.