Report the books store status: the catalog is served live via Gutendex (78,000+ books), plus the local D1 indexed-corpus counts (books with full text indexed, total indexed passages, last refresh timestamp).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond annotations by clarifying that the catalog is served live via Gutendex and that the D1 counts are local indexed-corpus statistics, including the last refresh timestamp. This helps the agent understand what data source and freshness semantics to expect.
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 a single, well-structured sentence that front-loads the core purpose ('Report the books store status') before listing the key data components. Every element adds value: data source, scale, indexed-corpus metrics, and refresh timestamp. There is no filler or redundant restatement of the tool name.
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 zero-parameter status-reporting tool, the description is complete. It tells the agent what to expect in the response: live catalog availability, indexed corpus counts, total passages, and last refresh timestamp. No output schema is present, but the description compensates well by outlining the return categories. No additional context seems necessary for correct invocation.
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 tool has zero parameters, so the baseline is 4. There are no parameter semantics to explain, and the description appropriately focuses on the output content instead. Because there is no input schema burden, the description does not need to compensate for undocumented parameters.
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 a clear resource ('books store status'), then enumerates exactly what the status contains: live Gutendex catalog, local D1 indexed-corpus counts, and last refresh timestamp. This clearly distinguishes it from sibling book tools like book_search, book_details, and book_fulltext_search, which operate on individual books or search results rather than the store's health.
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 implies this is the tool to use when an agent needs a high-level status or health summary of the books store, rather than specific book content. However, there is no explicit when-to-use guidance, no mention of when not to use it, and no reference to alternative sibling tools. The usage intent is clear enough but left implicit.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.