A chain's per-newsroom storyboards: for each outlet, every piece it filed on the story entry-by-entry, the desk's published READ of what each piece was built to do (objective label, motive with its mandatory innocent read, confidence), the label arc, and the desk's 'story, as built' synthesis where one exists.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | Yes | Chain slug, e.g. 'fauci'. |
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 present, the description carries the disclosure burden. It is transparent about the detailed returned content and flags optional data ('where one exists'), while the 'get' verb signals a read operation; however, it does not address edge cases like missing storyboards or any access constraints.
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 one dense sentence with no filler; each clause adds a distinct facet of the returned data. It front-loads the core resource ('chain's per-newsroom storyboards') before listing subcomponents.
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?
There is no output schema, so the description compensates by enumerating the main contents of the result: per-outlet entries, objective label, motive/read/confidence, label arc, and optional synthesis. Together with the schema's chain-slug definition, this is largely sufficient, though it omits any note on empty results or failure behavior.
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 single parameter 'chain' is fully documented in the schema with an example, and schema coverage is 100%. The description adds no new parameter-level detail, so it neither harms nor improves the schema's contribution.
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?
States a specific retrieval operation ('get') on a clearly bounded resource: a chain's per-newsroom storyboards, with an enumerated breakdown of what those boards contain. This is readily distinguishable from siblings like get_framing_index or get_claim_ledger because the output object is specified.
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 clear the tool is for fetching a chain's per-newsroom storyboards and takes a chain slug, which implies the trigger condition. It does not explicitly compare with alternative tools or state when not to use it, so the agent must infer selection from the resource name.
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.