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Openverse

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

A4.7/5.0
Behavior5/5

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

Discloses return structure (passages with offsets and similarity scores), embedding method (BGE-base-en, cosine, 500-char windows), and input cap (200K chars with truncation flag). Annotations already indicate safety, but description adds rich behavioral details.

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, focused paragraph with key idea upfront ('Semantic search INSIDE a fetched record'). Every sentence adds value, no redundancy, well-structured.

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 no output schema, description adequately explains return format (passages with offsets, similarity scores) and behavior (truncation flag). Covers all relevant aspects for agent decision-making.

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. Description adds useful context: default limit value (5), range (1-20), and example queries for the query parameter, going beyond schema descriptions.

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 it performs semantic search inside a fetched record, using action verb 'search' and specific resource 'INSIDE a fetched record'. Distinguishes from siblings like ask_pipeworx_grounded by highlighting its use for large records too big for prompts.

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 tells when to use: 'when the record is too big to cram into the prompt'. Mentions pairing with ask_pipeworx_grounded, implying alternative. Lacks explicit 'do not use' scenarios but provides clear context.

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

Several tools occupy the same functional space: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions from Pipeworx data, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The prediction-market tools also heavily overlap in purpose, as do ai_visibility_check and scan_competitor_ai_presence. Only the Openverse media tools and memory/subscription tools are cleanly distinguishable.

Naming Consistency3/5

All names are lowercase snake_case, but the naming conventions are mixed: verb_noun tools like search_images and resolve_entity coexist with bare verbs like remember, recall, forget, and subscribe, plus noun compounds like entity_profile, polymarket_edges, and bet_research. Subfamilies such as polymarket_* and the audio/image tools are internally consistent, but there is no single predictable pattern across the full set.

Tool Count2/5

37 tools exceeds the 25+ threshold and feels inflated for the surface, especially since several could be consolidated: there are three ask_pipeworx variants and five overlapping prediction-market tools. The Openverse-specific core is only 6 tools, with 31 mostly unrelated Pipeworx and utility tools attached, making the server feel like a grab-bag rather than a purpose-built Openverse integration.

Completeness4/5

Each embedded subdomain covers its main lifecycle well: Openverse has search/get/related for images and audio, memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and research has ask, grounded, deep_research, entity_profile, compare_entities, resolve_entity, and validate_claim. Minor gaps exist—notably no Openverse video/collection tooling and no explicit memory update—but agents can work around them without hitting dead ends.

Resources