Report a suspected error in a published piece (a miscount, a quote that reads wrong, a broken claim). Verified catches are fixed and logged on the public corrections ledger, credited 'Caught by a reader.' Requires the reader key.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | The reader key issued by the desk's operator. | |
| slug | No | Slug of the piece, if known. | |
| description | Yes | What looks wrong, and where. |
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?
No annotations are provided, so the description carries the full burden. It discloses the outcome of verified reports (fixed and logged on public corrections ledger), the attribution ('Caught by a reader'), and the auth requirement. This goes beyond the schema and gives the agent a realistic model of the tool's behavior.
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, zero filler, front-loaded with the action and target. Every clause earns its place: purpose, examples, outcome, and auth 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 3-parameter report tool, the description covers the essential behavior and outcome. It omits what the tool returns on submission, but with no output schema and low complexity, that is a minor gap. An agent can safely invoke it with the described context.
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 adds little beyond the schema: it reinforces that the key is required and gives examples of what a description should contain, but it doesn't meaningfully augment the parameter docs.
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 ('Report') and resource ('a suspected error in a published piece'), with concrete examples of what counts as an error. It is clearly distinct from sibling read/list tools and other submission tools like submit_letter or propose_development.
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 when to use the tool (for suspected errors in published pieces) and states a prerequisite ('Requires the reader key'). It doesn't explicitly name alternatives or exclusion cases, but the purpose is specific enough that an agent can route to it correctly.
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.