Provenance record for one piece: SHA-256 hashes of its canonical JSON and published HTML as of the last corpus sync. Re-hash the live artifacts at the piece's URL and compare to confirm what you read is what the desk published.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The piece's URL slug, e.g. 'mcconnell-proof-of-life-photo'. |
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, the description carries the full burden and does a good job: it discloses that the hashes are as-of the last corpus sync and that live re-hashing is required, signaling potential staleness. It does not discuss error cases or response format, but the read-only verification nature is clear.
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 compact sentences: the first states what the tool returns and its as-of-sync limitation, and the second explains how to use it for verification. Every sentence earns its place, with no filler or 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 single-parameter tool with no output schema, the description covers the core return value (hashes for JSON and HTML), the staleness caveat, and the required verification workflow. It could optionally mention output structure or missing-slug behavior, but nothing essential is missing.
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 already documents the slug parameter fully with an example, so the description does not need to add much. The phrase 'at the piece's URL' lightly ties the slug to the verification workflow, but the semantic load is primarily handled by the schema, meriting the baseline score.
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 resource (one piece) and the main purpose: providing SHA-256 hashes so the caller can verify that published artifacts match live content. It is easy to distinguish from audit or chain-related siblings, though it does not explicitly name or contrast any sibling tool.
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 gives a concrete usage scenario: re-hash the live artifacts at the piece's URL and compare to confirm what was read is what the desk published. It offers clear context for when to use the tool, but it does not mention when not to use it or suggest an alternative sibling.
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 maps to a distinct resource or action: list versus get for corpus discovery, chain versus claim ledger versus storyboards for chain analysis, framing index versus boxscore for statistics, and submit/report/propose for reader input. Even adjacent pairs like get_audit and verify_piece are clearly separated by their different purposes.
All tools follow a consistent verb_noun snake_case pattern, with get_* for detailed retrieval, list_* for summaries, and action verbs for reader-facing inputs. The verb choice reliably signals the operation type throughout.
At 15 tools, the set sits at the upper end of the ideal range, but every tool addresses a distinct facet of the desk's public surface: discovery, deep detail, provenance, coverage monitoring, and reader interaction. No tool feels redundant or decorative.
The surface covers discovery, retrieval, chain analysis, provenance verification, corrections, and reader interaction, forming a coherent workflow with no dead ends. Minor gaps exist: boxscore days are only enumerated off-server, and letters are exposed only as excerpts rather than individually retrievable records.