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Glama

LiveDataLink

book_get_text

Read-onlyIdempotent

Return the full text of an indexed book by Gutenberg id, paginated by passage. Use from_seq + max_passages to page through it. For books in the catalog that are NOT indexed locally, returns the public gutenberg.org plain-text URL so the agent can fetch it directly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
from_seqNoPassage index to start from (0-based, default 0).
gutenberg_idYesProject Gutenberg ebook id.
max_passagesNoMaximum passages to return per call (default 40, max 200).

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already mark the tool as read-only and idempotent. The description adds meaningful behavioral context beyond those annotations: results are paginated by passage, and non-indexed books return a gutenberg.org URL instead of text, which is an important non-obvious fallback behavior.

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?

Three concise sentences: the first states the core operation, the second gives the pagination pattern, and the third covers the fallback case. No filler or repetition of schema details.

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

Completeness4/5

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

Without an output schema, the description still explains both return modes: paginated text passages for indexed books and a plain-text URL for non-indexed books. It is sufficient for correct invocation, though a bit more detail about what constitutes a 'passage' would make it fully self-contained.

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

Parameters4/5

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

The schema already documents all three parameters at 100% coverage, so baseline is 3. The description adds value by explaining the pagination relationship between from_seq and max_passages and by tying gutenberg_id to the indexed-versus-URL fallback behavior, which the schema alone does not convey.

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 names a specific verb and resource: 'Return the full text of an indexed book by Gutenberg id'. It also clarifies the pagination model and the fallback behavior for non-indexed books, which distinguishes it from search-oriented siblings like book_fulltext_search and book_details.

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

Usage Guidelines4/5

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

The description gives direct usage guidance: 'Use from_seq + max_passages to page through it' and explains the alternate path for unindexed books by returning a URL. It does not explicitly name sibling alternatives or state when not to use this tool, but the context is clear for this retrieval action.

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.