Skip to main content
Glama

Artsmia

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

A4.9/5.0
Behavior5/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds technical details: BGE-base-en embeddings, cosine similarity over 500-char windows, 200K char cap with truncation flag, and return structure (offsets, similarity scores). No contradiction.

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?

Single paragraph with front-loaded purpose: first sentence defines core function. Then use case, pairing, and technical details. Every sentence adds value, 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?

Given the complexity (semantic search, truncation, offsets, grounding pairing), the description covers all essential aspects. No output schema exists, but the description explains return includes passages with offsets and similarity scores, which is sufficient.

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 extra meaning: explains that 'text' is the document text, 'query' is natural-language with examples, and 'limit' has default and range. It also adds behavioral context about truncation.

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' with a specific verb ('search inside') and resource ('record text'). It also distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded.

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 tells when to use: 'Use when the record is too big to cram into the prompt.' It also describes pairing with ask_pipeworx_grounded for grounding, providing clear usage context.

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

All Artsmia toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) all target prediction-market analysis with fuzzy boundaries. ai_visibility_check and scan_competitor_ai_presence overlap as well. While some tools are clearly distinct (art search vs memory), the set as a whole requires careful reading to avoid misselection.

Naming Consistency2/5

Tool names follow multiple patterns: verb_noun (search_artworks, resolve_entity, validate_claim), domain_prefixed (polymarket_*, pipeworx_*), and product-style names (ask_pipeworx, bet_research, deep_research). Versioned suffixes like ask_pipeworx_beta and ask_pipeworx_grounded break any unified convention. Even though subgroups are internally consistent, the overall pattern is mixed and unpredictable.

Tool Count2/5

34 tools is on the high side, especially for a server named after an art museum. Only 3 tools actually relate to the Minneapolis Institute of Art, while the rest are a general-purpose data platform, prediction-market analysis, and memory/subscription features. Many of these extra tools are redundant or power-user variations, making the count feel inflated relative to the apparent domain.

Completeness3/5

For the stated art domain, the read-only surface (search, get, department highlights) is functional but thin — no artist browse, exhibitions, or advanced filtering. The broader data tools are comprehensive in themselves, but their presence distracts from the core domain and creates confusion about the server's intended purpose. The art coverage is adequate for basic queries but lacks depth expected from a dedicated museum collection API.

Resources