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Glama

LiveDataLink

by io.github.blackboxfoundry

book_search

Read-onlyIdempotent

Search the full Project Gutenberg catalog (~78,500 public-domain books) live via Gutendex by title / author / subject keyword, with optional author, subject, and language filters. Ranked by keyword relevance then download popularity. Returns each book's Gutenberg id, title, author(s), language, and download count. Use book_fulltext_search to search inside the locally indexed top books.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return (default 25, max 100).
queryNoTitle / author / subject keyword, e.g. 'frankenstein', 'sherlock holmes', 'astronomy'.
authorNoOptional author-name fragment, e.g. 'Shelley', 'Twain'.
subjectNoOptional subject fragment, e.g. 'Science fiction', 'Detective'.
languageNoOptional language code filter, e.g. 'en', 'fr'.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior, so the description adds useful behavioral context beyond them: live Gutendex source, ranking by keyword relevance then download popularity, and the exact fields returned. This gives the agent a strong sense of what the tool will do and what 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.

Conciseness5/5

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

The description is three dense sentences with no filler. The core search scope and filters come first, followed by ranking behavior, return fields, and the sibling-tool routing at the end. Every sentence earns its place.

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?

There is no output schema, so the description compensates by listing the returned fields. It also explains scope, source, ranking behavior, and the relevant alternative tool. For a search tool with optional filters and safety annotations, this is complete enough for an agent to select and invoke it 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%, with each parameter already documented with examples and defaults. The description mostly restates that query accepts title/author/subject keywords and that author, subject, and language are optional filters, without adding significant new parameter-level meaning 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 states a specific action ('Search the full Project Gutenberg catalog') with a clear resource, scope, and method (Gutendex). It explicitly distinguishes itself from book_fulltext_search by naming that sibling and clarifying the different search domain (metadata vs full text).

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

Usage Guidelines5/5

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

The description explicitly tells the agent to use book_fulltext_search when the task is to search inside book text, providing a clear routing rule. It also characterizes book_search as a live catalog search, so an agent can infer when this tool is appropriate versus sibling 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

B3.3/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources