Tip the desk that a story moved — a development on an existing chain or a story worth auditing. Lands in the same proposed-developments queue the observatory's triggers feed; the desk decides what, if anything, to file. Requires the reader key.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | The reader key issued by the desk's operator. | |
| tip | Yes | The development, with a source if you have one. | |
| chain | No | Chain slug, if the tip extends a chain. |
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 behavioral burden. It discloses that the tip only lands in a queue, that the desk decides whether anything is filed, and that a reader key is required. This clarifies the tool is a suggestion mechanism, not a direct filing action.
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 sentences with no filler. The action and target are front-loaded, and the second sentence efficiently adds queue behavior, desk discretion, and the authentication requirement.
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 three-parameter tool with no output schema, the description provides enough context to call it correctly: purpose, queue destination, desk discretion, and auth. It could mention the response or lack of confirmation, but that is not essential for making the call.
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 100%, so the baseline is 3. The description restates the reader-key requirement but adds little beyond the schema's own field documentation for tip and chain.
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 opens with a specific action ('Tip the desk') and names the resource ('a development on an existing chain or a story worth auditing'). It also situates the tool in the proposed-developments queue, distinguishing it from sibling write tools like submit_letter and report_error.
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 first clause implies when to use it — when a story has moved or is worth auditing — and the queue explanation clarifies what happens after the call. It does not explicitly exclude alternatives or name sibling tools, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.