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Notion_connect

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,752 across 1503 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

TDQS

A4.7/5.0
Behavior5/5

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

Even though annotations already cover read-only/idempotent safety, the description adds critical behavior beyond annotations: refusal mechanics, refusal reasons, verbatim evidence extraction, and the guarantee not to invent facts. This fully discloses what an agent can expect, and there is no contradiction with the 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 detailed yet tightly structured: core behavior first, success/refusal formats next, usage context and cost tradeoff last. Every sentence earns its place and no information is redundant with the schema or annotations.

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?

With no output schema present, the description fully documents both success and refusal return shapes, including the enumeration of refusal reasons. It also covers routing behavior, source breadth, evidence handling, and cost, making the tool fully callable by an agent without additional inference.

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?

The input schema already documents all six parameters as aliases for 'question' with 100% coverage, so the baseline is 3. The description confirms the question is natural language and that the tool routes it like ask_pipeworx, but it does not add any new parameter-level semantics beyond what the schema provides.

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 uses a specific verb ('extracts') and a clear resource, and explicitly distinguishes this mode from ask_pipeworx by naming the same routing and the added grounded-extraction behavior. It also states the exact return and refusal shapes, leaving no ambiguity about what the tool does.

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 when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' for high-stakes reads, and contrasts with casual lookups by saying to prefer ask_pipeworx. It also notes the extra LLM call cost, which is actionable for tool selection.

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

A3.6/5.0
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