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Glama

Epa Echo

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?

Annotations already mark the tool as read-only, open-world, and idempotent. The description adds substantial detail: it specifies the embedding model (BGE-base-en), chunking method (500-char overlapping windows), and a hard cap of 200K characters with truncation and flagging. All behaviors are disclosed without contradicting 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?

The description is four sentences long, with the first sentence immediately stating the tool's core function. Every sentence adds necessary information (use case, return values, pairing, technical details). No redundant or extraneous content.

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 return values: 'top-N passages with character offsets and similarity scores.' It also covers the pairing with ask_pipeworx_grounded, technical constraints (200K cap, embedding model), and the intended workflow. For a tool with three parameters and no output schema, this is exhaustive.

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% with clear descriptions. The description adds context for each parameter beyond the schema: for 'text' it says 'the text you already pulled', for 'query' it provides example natural-language queries, and for 'limit' it explains the range and default. This adds meaningful value but is not essential since the schema is already strong.

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 the tool performs semantic search inside a fetched record, specifically targeting scenarios where the record is too large for the prompt. It distinguishes itself from the sibling tool ask_pipeworx_grounded by explaining the pairing and the value of character offsets.

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 states when to use the tool: 'Use when the record is too big to cram into the prompt.' It also provides practical guidance on pairing with ask_pipeworx_grounded and explains that it saves context by returning only relevant passages.

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

A3.9/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded form a tight cluster, and the beta variant is explicitly identical to ask_pipeworx today. Several meta-tools like discover_tools, suggest_questions, and deep_research also overlap in discovery-oriented usage, so an agent must read carefully to pick the right one.

Naming Consistency3/5

Names are uniformly lowercase with underscores and mostly descriptive, but the conventions are mixed: verb_noun tools like validate_claim and list_subscriptions sit alongside noun_phrase tools like entity_profile and polymarket_arbitrage, plus bare verbs like remember and subscribe. It is readable but not a single predictable pattern.

Tool Count2/5

36 tools is well above the 25+ threshold, and the surface spans unrelated domains: EPA ECHO data, general Pipeworx research, Polymarket betting, memory, npm scanning, and AI visibility checks. For a server named 'Epa Echo', most tools feel out of scope and the collection seems like several separate servers merged together.

Completeness4/5

The EPA ECHO subset provides a solid facility-search, violations, compliance-history, and enforcement-action lifecycle. The broader Pipeworx surface also covers lookups, grounded answers, deep research, entity profiling, subscriptions, and memory, with only minor workaround-level gaps such as no dedicated ECHO permit/emissions detail tool.

Resources