verify
Verify a claim / the ledger hash chain against the Bitcoin-anchored record.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| claim_hash | No |
Verify a claim / the ledger hash chain against the Bitcoin-anchored record.
| Name | Required | Description | Default |
|---|---|---|---|
| claim_hash | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context (the verification is anchored to a Bitcoin record), but it does not disclose behavior beyond that, such as failure modes, required preconditions, or what happens when the claim is not found.
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 a single, front-loaded sentence with no filler or repetition. The main weakness is the slash ambiguity between 'claim' and 'ledger hash chain,' which slightly reduces precision without adding significant length.
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 one-parameter, read-only tool, the description is minimally viable: it names the operation and the anchor. Still, it omits usage guidance, return semantics, and parameter details, which are needed because there is no output schema and schema coverage is 0%.
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 needs to compensate. It never explains claim_hash's format, semantics, optionality, or relationship to a 'claim' vs the 'ledger hash chain.' The parameter name is suggestive, but not enough for an agent to confidently construct the value.
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 a specific verb ('Verify') and identifies a resource ('a claim / the ledger hash chain against the Bitcoin-anchored record'). However, the slash introduces ambiguity about whether it verifies a claim, a ledger hash chain, or a claim expressed as a ledger hash, and it does not clearly differentiate from siblings like check_hypothesis or ground_truth.
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 guidance is given on when to use this tool versus the many validation-related siblings (check_hypothesis, ground_truth, ground_stability). There are no exclusions, prerequisites, or alternative-selection cues, so an agent must guess which tool fits a given task.
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.