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Uk Parliament

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds that it calls ai_visibility_check internally and returns specific fields (score, confidence, signal density). No contradictions.

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 two sentences, front-loaded with the main action, and contains no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although there is no output schema, the description specifies the return format (ranked list with score, confidence, signal density). The simple read-only nature and good annotations mean the description is sufficiently complete.

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% with each parameter described. The description adds that the first entity is treated as the 'subject' for narrative, which is not in the schema. This adds meaningful semantics.

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 the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, and returns a ranked list. It distinguishes itself from the sibling ai_visibility_check by being a batch comparison.

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?

The description provides clear context for use (competitive AI-marketing audits) and implies that for single entities one should use ai_visibility_check. It does not explicitly state when not to use, but the use case is well-defined.

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

B3.4/5.0
Disambiguation2/5

Several tools are near-duplicates (ask_pipeworx and ask_pipeworx_beta are currently identical; ai_visibility_check vs scan_competitor_ai_presence overlap), and the massive mix of unrelated domains (Polymarket betting, general data lookup, AI visibility) alongside UK Parliament tools makes selection confusing. An agent would struggle to know whether to use ask_pipeworx, ask_pipeworx_beta, or ask_pipeworx_grounded, or which of the five polymarket tools fits.

Naming Consistency3/5

Most tools use snake_case with readable names, but the action placement varies (verb_noun like get_bill vs noun_verb like bet_research, entity_profile), and there are compound names like generate_llms_txt and scan_competitor_ai_presence. The style is mostly consistent but the verb_noun pattern is not uniform across the set.

Tool Count2/5

38 tools is heavy, and the overwhelming majority are unrelated to the server's stated UK Parliament purpose. Only 7 tools (get_bill, search_bills, bill_stages, get_member, search_members, search_hansard, recent_divisions) have anything to do with Parliament, making the count wildly inappropriate for the apparent scope.

Completeness2/5

The Parliament-specific surface is thin: basic bill/member/Hansard lookups exist, but there are no tools for specific divisions/votes, committees, publications, or detailed procedural information. The vast non-Parliament tooling is irrelevant, creating a dead end for any real Parliament research beyond the basics.

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