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Glama

The Stochastic Parrot

list_audits

List published pieces (summaries: slug, title, kind, date, counts of hard contradictions and naming/framing splits), newest first. All filters optional and combinable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoOnly pieces of this kind: 'audit' (discrepancy audit), 'coverage' (coverage brief), 'dispatch', or 'solo' (solo audit).
limitNoMaximum results to return (default 25, hard cap 100).
sinceNoOnly pieces dated on or after this ISO date (YYYY-MM-DD).
untilNoOnly pieces dated on or before this ISO date (YYYY-MM-DD).
outletNoOnly pieces whose sources include this outlet, matched exactly but case-insensitively, e.g. 'Reuters' or 'Fox News'.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by specifying that only published pieces are listed, results are summaries rather than full content, ordering is newest first, and all filters can be combined. It does not disclose potential pagination behavior or error conditions, but the core behavior is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The entire description is one compact, front-loaded sentence that communicates the core action, return summary fields, ordering, and filter behavior. There is no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description explains what the tool returns (summaries with specific fields), how they are ordered, and that all filters are optional and combinable. With five parameters fully documented in the schema and no required parameters, the description is sufficiently complete for an agent to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all five parameters thoroughly. The description adds a useful behavioral note that filters are optional and combinable, but it does not provide additional parameter-level semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'List published pieces' and enumerates the exact summary fields returned (slug, title, kind, date, counts of hard contradictions and naming/framing splits), making the resource and scope specific. The ordering ('newest first') and filter behavior further distinguish this from sibling tools like get_audit or search_corpus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly states that filters are optional and combinable, which gives useful invocation guidance. However, it does not explicitly tell the agent when to use this tool versus alternatives like get_audit, list_corrections, or search_corpus, or what kinds of tasks are better suited to those tools.

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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TDQS

A4/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Resources