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Bundlephobia

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 5,743 across 1500 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

A4.6/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds substantial context: it returns verbatim evidence and confidence, refuses with specific refusal_reason values when the data doesn't answer, and costs one extra LLM call. No contradiction with annotations.

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?

The description is longer than minimal but every sentence carries operational value: behavior, output shape, refusal conditions, and usage guidance. It is front-loaded with the core promise, though some internal details (tool count, argument filling) could be trimmed without losing essential guidance.

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?

No output schema exists, yet the description fully documents the success and refusal return shapes. It also covers when to prefer this tool, the extra cost, and the routing behavior, leaving no material gap for an agent deciding whether and how to invoke it.

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 description coverage is 100%: all parameter aliases are documented as aliases for question, and the question parameter explains the aliases. The description adds no additional parameter semantics, but the schema already fully covers it, so the baseline of 3 applies.

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 describes a specific tool behavior: a hallucination-resistant, grounded answer mode that routes like ask_pipeworx but extracts answers only from tool results. It differentiates itself from the sibling ask_pipeworx by emphasizing evidence-based, refusal-capable answers.

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 states when to use: high-stakes reads where answers will be quoted, cited, or acted on. It also names the alternative (ask_pipeworx) and the trade-off (extra LLM call), directing casual lookups elsewhere.

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

There is substantial overlap among tools in the Pipeworx group: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route natural-language queries to the same 5,578 tools and sources, with only subtle differences in mode (beta vs stable, grounded vs standard, single vs multi-part). Similarly, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk are heavily intertwined, making differentiation difficult. Tools like similar, size, history, and scan_dependency from the bundlephobia side are distinct, but the Pipeworx family muddies the set.

Naming Consistency3/5

The bundlephobia tools follow a consistent noun pattern (size, similar, history), and the Pipeworx meta-tools use snake_case verbs (ask_pipeworx, resolve_entity, compare_entities, validate_claim). However, the naming is inconsistent across the two families—bundlephobia's simple nouns (size, similar, history) clash with the verbose descriptive verbs—and naming like ai_visibility_check, scan_competitor_ai_presence, and generate_llms_txt break from the Pipeworx pattern. The set mixes short names, camelCase-ish compounds, and snake_case, so no single consistent convention holds.

Tool Count2/5

35 tools is too many for a server that ostensibly serves two domains (bundle-size analysis and Pipeworx data research). The bundle-size analysis needs only a handful (size, history, similar, recent_searches, scan_dependency), yet there are over 30 tools dominated by a sprawling meta-research layer including multiple ask_pipeworx variants, several polymarket tools, plus meta-cognitive tools (remember, recall, forget, discover_tools) that are not core to either domain. This bloats the surface and makes call routing difficult.

Completeness4/5

Each functional domain is fairly complete: bundlephobia covers size measurement, history, alternatives, search, and dependency vetting; the Pipeworx side covers lookup, research, entity resolution, comparison, verification, subscriptions, and feedback. However, there are gaps—e.g., no tool for directly reading an npm package's README or license beyond scan_dependency's summary, and no explicit tools for some administrative actions like account management or subscription editing beyond create/cancel/list. The completeness is strong for what's advertised but not exhaustive.

Resources