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Glama

Shopify

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

A4.2/5.0
Behavior4/5

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

Annotations already declare idempotent and read-only behavior. Description adds specific technical details: BGE-base embeddings, cosine similarity, 500-char windows, 200K char cap with truncation flag. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise and front-loaded with core purpose. Technical details are included but not overly verbose. Well-structured for agent comprehension.

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?

Covers purpose, usage context, input parameters, technical behavior, limitations (truncation), and relationship to sibling tool. Sufficient for agent to use correctly without output schema.

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 3 applies. Description reinforces inputs but adds minimal extra meaning beyond schema descriptions (e.g., max ~200K chars already in schema).

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, returns top-N passages with offsets and scores. Differentiates from siblings by specifying pairing with ask_pipeworx_grounded and contrasting with other memory tools.

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?

Explains when to use (record too big for prompt) and pairs with another tool. Lacks explicit exclusions or alternatives, but provides strong contextual guidance.

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

Many tools overlap in purpose, especially the Pipeworx data retrieval tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and the Polymarket betting tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). The Shopify-specific tools are distinct, but the overall set is a confusing mix of domains, making it hard for an agent to select the right tool.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use snake_case (ai_visibility_check, bet_research), others use underscores in various patterns (generate_llms_txt, scan_competitor_ai_presence). The Shopify tools use a shopify_ prefix, but the rest follow no uniform scheme, making it unpredictable.

Tool Count1/5

35 tools is far too many for a server named 'Shopify'. Only 5 tools are Shopify-specific; the rest are general-purpose data tools (Pipeworx, Polymarket, memory, etc.). This severe scope mismatch makes the tool count inappropriate and overwhelming for the intended domain.

Completeness2/5

As a Shopify server, the tools cover only basic read operations (list/get products, orders, customers), missing crucial write operations (create, update, delete) and other Shopify features (webhooks, inventory, etc.). The general tools cover many domains but are not integrated into a coherent Shopify workflow.

Resources