GET the permissionless public-root at https://councilof.ai/root.json. Returns merkle_root, card_count, as_of, and UNMEASURED notes. Separate from GSPC. Three states: VALID (fetched), UNREACHABLE (could not fetch), UNCHECKABLE (body unreadable). Never a certificate.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
With no annotations provided, the description carries the full behavioral burden and does so well. It discloses permissionless access, enumerates the three possible result states (VALID, UNREACHABLE, UNCHECKABLE), and adds a meaningful non-obvious fact: the result is never a certificate.
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 front-loaded with the core action and URL. Each sentence adds distinct value, though 'UNMEASURED notes' and 'GSPC' are used without explanation, slightly reducing accessibility.
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 parameterless GET tool with no output schema and no annotations, the description is remarkably complete: it gives the endpoint, returned fields, possible states, and a critical exclusion. An agent has enough information to invoke the tool and interpret its result 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 tool has zero parameters, so there is no parameter semantics to document. The description instead clarifies the fixed endpoint and return shape, which fully compensates for the empty schema. This matches the baseline for a parameterless tool.
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 names a specific verb (GET), a specific resource (https://councilof.ai/root.json), and lists the exact returned fields (merkle_root, card_count, as_of, UNMEASURED notes). It also clarifies what this tool is not ('Separate from GSPC' and 'Never a certificate'), helping distinguish it from related concepts.
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 makes the tool's purpose clear—fetch the permissionless public-root—so when to use it is implied. However, it does not explicitly state when to prefer this tool over alternatives or mention any sibling tools by name; 'Separate from GSPC' hints at distinction but does not provide concrete routing guidance.
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.
Each tool has a distinct target resource or action, but some boundaries require careful reading: get_card, verify_card, and verify_inclusion all deal with cards/proofs, while art50_marking_evidence and rwa_evidence are both paid evidence packs. The detailed descriptions help separate them, so misselection is unlikely but not impossible.
There are clear verb-prefixed clusters like get_axis/get_card/get_root and verify_card/verify_inclusion, but several tools use bare noun phrases: board_totals, commission_card, receipts_batch, rwa_evidence, and art50_marking_evidence. The naming is readable and snake_case throughout, but the verb/noun convention is mixed.
With 11 tools, the set is well-scoped for a GSPC board, measurement-card, evidence, and verification server. Each tool serves a distinct endpoint or workflow, and none feels redundant or purely decorative.
The surface covers board totals, axis details, roots, card retrieval, verification, paid evidence, and historical receipts. A minor gap is the absence of a list_axes tool to enumerate all board axes or a direct get_measurement_card endpoint, but existing tools can work around this via list_cards and verify_card.