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Seo Competitors

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses the exact success and refusal return shapes, enumerated refusal reasons, dependence on tool result content, and an added LLM call cost. This goes well beyond the annotations and gives an agent a realistic model of tool behavior.

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 dense but every sentence contributes: purpose, routing behavior, success/refusal shapes, exact refusal reasons, use cases, and cost trade-off. It is front-loaded with the core differentiating trait and avoids filler.

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 absence of an output schema, the description fully compensates by describing the return shape and refusal enum. It also covers when to use the tool, how it differs from its sibling, and its cost, leaving no critical operational gap for an agent to infer.

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 parameters at 100% coverage, including aliases. The description adds no parameter-specific meaning beyond implying a natural-language question is the input, which is already in the schema, 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?

The description clearly identifies the tool as a hallucination-resistant answer mode that routes to the appropriate tool and extracts answers strictly from the tool result. It differentiates itself from sibling ask_pipeworx by emphasizing grounded extraction and explicit refusals rather than casual lookup behavior.

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?

It explicitly states when to use this tool: whenever an answer will be quoted, cited, or acted on and facts must not be invented, with examples. It also contrasts with ask_pipeworx and advises preferring that sibling for casual lookups, making the selection criteria clear.

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

All Seo Competitors toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools are near-duplicates or easily confused: ask_pipeworx_beta is explicitly identical to ask_pipeworx, ai_visibility_check overlaps with scan_competitor_ai_presence, and polymarket_edges, polymarket_arbitrage, and bet_research all target opportunity discovery. While many tools are distinct, the boundaries between these clusters are unclear enough to cause misselection.

Naming Consistency4/5

The vast majority of tools use a consistent lowercase snake_case convention with descriptive noun/verb patterns (e.g., polymarket_edges, entity_profile, validate_claim, resolve_entity). Minor deviations like seo_domain_ranked_keywords and ask_pipeworx_beta are slightly off-pattern, but the overall style is predictable.

Tool Count2/5

32 tools is above the threshold where a set starts to feel bloated, especially for a server named "Seo Competitors". The count includes many unrelated subsystems—Polymarket betting, memory, subscriptions, and generic data routing—making it feel like a kitchen sink rather than a focused SEO competitor toolkit.

Completeness2/5

For a server claiming to support SEO competitor analysis, the surface is incomplete: it offers a keyword-ranking tool and AI visibility checks, but lacks standard competitor SEO capabilities like backlink analysis, rank tracking over time, content-gap analysis, or site audits. The broader data/query tooling is extensive, but it doesn't fill the gaps in the advertised domain.

Resources