List DeepMap's documented nulls / bounds / retractions (the Null Museum), optionally by domain -- each with the positive control that makes it trustworthy. We carry our own retractions; that is the credibility.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| domain | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds meaningful behavioral context: each returned null/bound/retraction includes a positive control for trustworthiness, and the system 'carries its own retractions.' This goes beyond the 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?
The description is short and front-loaded, with the core action and resource in the first sentence. The second sentence is somewhat rhetorical but compact and reinforces the trust/credibility angle.
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 read-only list tool with no output schema, the description covers the main operation and return content at a high level. However, 'limit' semantics and valid domain format are left unspecified, and the absence of an output schema means an agent must infer the response structure from the prose.
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 0%, so the description must carry the weight for both parameters. It clarifies 'domain' by saying the listing is 'optionally by domain', but it never explains the 'limit' parameter, its default, or acceptable domain values.
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: 'List DeepMap's documented nulls / bounds / retractions (the Null Museum)'. It also adds the optional domain filter, making the tool's purpose clear. It does not explicitly differentiate from siblings, but the resource is distinctive enough that an agent can tell it apart.
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 by describing a listing operation with an optional domain filter. However, it does not state when to prefer this tool over siblings or mention any exclusions, leaving the decision to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
ground_truth aggregates the same water/stability/hazard/resource answers offered by individual tools, and check_hypothesis/verify/list_nulls all inhabit the trust/provenance space, so an agent may hesitate between options. The descriptions are detailed enough to resolve most selections, but the overlap is real and not just one or two edge cases.
All names use snake_case, but the style is mixed: some are bare nouns or noun phrases (capabilities, ground_stability, water_availability) while others are imperative verbs (check_hypothesis, list_nulls, run_controlled_test, verify). This is readable but lacks a single consistent verb_noun convention.
Eleven tools is a well-scoped size for an Earth-grounding service; each tool earns its place by covering a distinct capability like querying hazards, checking hypotheses, running controlled tests, or verifying anchors. The set is neither bloated nor thin.
The set covers the advertised five-question answer surface plus verification, hypothesis checking, nulls, and space-weather risk, giving agents a solid query-test-verify lifecycle. Minor gaps exist around contributing new nulls or hypotheses, but the core workflows have no dead ends.