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Glama

Europeana

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?

Annotations already declare safe read-only behavior. The description adds rich behavioral context: embedding model (BGE-base-en), windowing (500-char overlapping), character offsets, similarity scores, truncation with flag, and 200K char cap. This goes well beyond what annotations provide.

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?

Two sentences, no wasted words. The first sentence immediately conveys the core purpose. Every clause adds distinct information (what it does, when to use, how it works, limitations).

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?

Despite lacking an output schema, the description explains the return format (passages with character offsets and similarity scores). It covers embedding details, truncation, pairing with another tool, and usage guidance. This is fully adequate for an agent to invoke correctly.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying the 'text' parameter's max length, providing example queries for 'query', and specifying the range for 'limit'. This enhancement justifies a 4.

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 uses a specific verb ('search inside') and resource ('a fetched record'), and distinguishes from sibling tools like 'ask_pipeworx_grounded' by explaining they pair together. Examples of use cases (SEC 10-K, article) further clarify the purpose.

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?

Explicitly says 'Use when the record is too big to cram into the prompt' and describes how it pairs with another tool. However, it does not explicitly state when not to use it or list alternatives beyond the one mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Europeana toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., three versions of ask_pipeworx for similar tasks, and several research/analysis tools (bet_research, deep_research, entity_profile) with unclear boundaries. The mix of Europeana-specific tools with a broad research toolkit creates confusion.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ai_visibility_check, ask_pipeworx), verb_noun patterns (compare_entities, resolve_entity), and short names (search, record, forget). No clear naming convention is followed throughout the set.

Tool Count2/5

33 tools is excessive for a Europeana-focused server, as only 3 tools (search, record, search_within) are directly related to Europeana. The rest are a broad, unrelated toolkit, making the server feel like a kitchen sink rather than a cohesive collection.

Completeness2/5

The Europeana-specific tool surface is severely incomplete, lacking browse collections, advanced filters, or entity linking. The inclusion of many unrelated tools does not compensate for the gaps in the core domain, resulting in a poorly scoped offering.

Resources