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Glama

Entso E

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Discloses embedding model (BGE-base-en), windowing (500-char overlapping), similarity metric (cosine), character cap (200K) with truncation and flagging, and output details (passages with offsets and scores). Adds significant context 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?

Four sentences, front-loaded with main idea, each sentence adds unique information (purpose, use case, behavior, constraints, pairing). No wasted words.

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?

For a tool with 3 parameters and no output schema, the description covers input format, processing details, output structure, constraints, and integration with a sibling tool. Sufficient for an agent to understand and 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%; description repeats the text max (~200K chars) but adds workflow context (e.g., 'the text you already pulled', 'natural-language query') and links parameters to the output (top-N passages with offsets and scores). Provides moderate additional meaning.

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?

Clearly states 'Semantic search INSIDE a fetched record' with specific examples (SEC 10-K, article, long tool result). Distinguishes from siblings by describing pairing with ask_pipeworx_grounded and the inside-a-record use case.

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?

Explicitly says 'Use when the record is too big to cram into the prompt' and explains how it pairs with ask_pipeworx_grounded for grounding over relevant passages, providing clear when-to-use and alternative workflow.

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

B3.1/5.0
Disambiguation2/5

Several tools have overlapping or duplicate roles: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and deep_research/ask_pipeworx/discover_tools/suggest_questions all serve query routing. The five ENTSO-E tools are distinct but are lost among the unrelated Pipeworx/prediction-market tooling.

Naming Consistency2/5

Naming mixes single verbs (remember, forget, subscribe), noun phrases (actual_load, entity_profile), verb_noun patterns (compare_entities, discover_tools), and brand-prefixed groups (pipeworx_*, polymarket_*). Snake_case is consistent, but the verb style and naming logic vary widely with no discernible overall pattern.

Tool Count2/5

36 tools is too many for a server supposedly focused on ENTSO-E electricity data, especially since only 5 tools serve that domain. Even as a general data-access server, the set is heavy and includes redundant/beta variants (ask_pipeworx_beta, ask_pipeworx_grounded) that inflate the count.

Completeness1/5

The server name 'Entso E' implies electricity-market data, but only 5 of 36 tools cover generation, load, prices, capacity, and cross-border flow. Missing typical ENTSO-E operations like forecasts, balancing, or real-time grid status, while the remaining 31 tools belong to an unrelated data platform — severely incomplete for the advertised purpose.

Resources