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Glama

Bookbrainz

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description adds significant behavioral details beyond annotations: truncation at 200K chars with flagging, embedding model BGE-base-en, 500-char overlapping windows, cosine similarity, and output includes character offsets and similarity scores. This is comprehensive and goes well beyond the annotations.

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?

Approximately 100 words, front-loaded with core purpose. Every sentence adds value, from usage guidance to technical details. No redundancy or filler.

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 return values (passages with offsets and similarity scores) and constraints (truncation, chunking). This covers all necessary context for an agent to use the tool appropriately.

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 coverage is 100%, so baseline is 3. The description adds examples for 'query' and emphasizes the max length for 'text', but does not significantly enhance meaning beyond the schema. The added behavioral context indirectly helps but is not directly parameter semantic.

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 clearly states 'Semantic search INSIDE a fetched record' and explains the use case of searching within large documents. It distinguishes the tool from siblings like ask_pipeworx_grounded by specifying the verb 'search' and the resource 'a fetched record', making the purpose specific and unambiguous.

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 when to use: 'Use when the record is too big to cram into the prompt' and contrasts with ask_pipeworx_grounded, explaining how they pair. This provides clear context and alternatives for the agent.

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

A3.8/5.0
Disambiguation2/5

The tool set mixes three distinct domains: BookBrainz entity access (browse, lookup, search), Pipeworx data routing (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions), and Polymarket betting analysis. Multiple tools have overlapping purposes, particularly the ask_pipeworx variants and the various polymarket_* tools, which an agent could easily confuse.

Naming Consistency4/5

All tool names follow a readable snake_case convention. While some are single verbs (browse, lookup, search), others use compound patterns (ask_pipeworx_grounded, polymarket_edge_tracker, pipeworx_trending), and there are a few noun-first names (entity_profile, pipeworx_feedback, recent_changes). The inconsistency is minor and does not hinder readability.

Tool Count2/5

With 34 tools, this is a large surface for a server named Bookbrainz, yet only 3 tools (browse, lookup, search) actually serve that domain. The remaining 31 tools are unrelated Pipeworx/Polymarket functionality, making the tool count inappropriate for the stated server purpose and suggesting a mislabeled or overloaded server.

Completeness2/5

The BookBrainz domain is severely incomplete: it offers read-only access (search, browse, lookup) but no create, update, or delete operations for entities. For the broader Pipeworx/Polymarket functionality, coverage is quite deep, but that is not the server's stated domain. This leaves a fundamental gap for the BookBrainz use case.

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