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Notion_connect

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".

TDQS

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, etc.), the description discloses key behaviors: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K character cap with truncation and flagging. It also describes the return format (passages with offsets and similarity scores), providing full transparency.

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 concise yet comprehensive. It uses bold for key terms, is front-loaded with the purpose, and every sentence adds value (usage scenario, pairing, technical details, limitations). No redundant or vague statements.

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, the description adequately covers return values (passages with offsets and scores). It explains the algorithm, truncation, and pairing with another tool. For a tool with moderate complexity, this completeness is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Though schema coverage is 100%, the description adds significant context: text is 'already fetched', query is natural language, limit is top-N passages. It explains the truncation behavior for text and the typical use case, enhancing understanding beyond the schema definitions.

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 that this tool performs semantic search inside a previously fetched record, returning passages with offsets and scores. It distinguishes itself by explicitly contrasting with using the whole record in the prompt, and mentions pairing with ask_pipeworx_grounded, which differentiates it from sibling tools like ask_pipeworx.

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 gives explicit guidance: use when the record is too large for the prompt, and pairs with ask_pipeworx_grounded for grounding. It explains that it saves context and returns only relevant passages, helping agents decide when to use this tool instead of alternatives.

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

All Notion_connect toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same 5,708 tools, with beta explicitly described as currently identical to the stable version. Prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage) also blur together, and the Notion tools are a small island in a sea of unrelated Pipeworx utilities.

Naming Consistency3/5

The names are uniformly lowercase with underscores, but conventions are mixed: some use verb-first patterns (ask_pipeworx, generate_llms_txt, scan_dependency), others are noun-phrases (entity_profile, recent_changes, polymarket_edges), and domain prefixes are inconsistent (notion_*, polymarket_*, pipeworx_*, but bare bet_research, compare_entities, recall). It is readable but lacks a coherent naming scheme.

Tool Count2/5

36 tools is already heavy, but the bigger issue is scope: the server is named Notion_connect yet only 5 of 36 tools relate to Notion. The rest span data research, prediction markets, memory, subscriptions, AI visibility, npm auditing, and llms.txt generation — a grab bag far beyond any single purpose, with multiple redundant meta-tools inflating the count.

Completeness2/5

As a Notion connector it is severely incomplete: there is no create/update/delete for pages or databases, and no way to write content back to Notion — only read/search/query operations. For the broader Pipeworx surface, the tool set is sprawling but unfocused, so it is hard to identify a coherent domain where coverage could be considered complete.

Resources