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Politics Feeds

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds critical behavioral context: the financial implication of using Anthropic (BYO key, pay directly), the return shape (per-model fields + combined view), and the default free model. No contradictions 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each dense with information. The description is well-structured: opening with the core action, explaining key parameters, and ending with use cases. No redundant phrases.

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?

Given the tool's moderate complexity (multiple models, scoring), the description covers the return structure (per-model + combined) and usage contexts. No output schema exists, but the description compensates adequately. Slightly more detail on scoring methodology (0-100 scale interpretation) would elevate to 5.

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%, so baseline is 3. The description enhances understanding by explaining the default model for 'models', the role of '_apiKey' as payment shim, and 'context' as disambiguation aid. This adds meaningful guidance beyond the schema alone.

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 specific action verbs ('Probe', 'score') and clearly identifies the resource (LLMs' knowledge about an entity). It does not simply restate the name; it explains the purpose and distinguishes from potential siblings like 'scan_competitor_ai_presence' by focusing on visibility scoring across models.

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 explicitly states use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'). It also implies when not to use (e.g., if no Anthropic key, default model works). However, it does not directly contrast with sibling tools like 'compare_entities' or 'deep_research', leaving some ambiguity.

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

All Politics Feeds toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Most tools have distinct jobs and the descriptions are unusually explicit about routing, but there are overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all sit in the same query/research space. ask_pipeworx_beta even states it currently matches ask_pipeworx exactly, which makes some boundaries genuinely ambiguous despite strong descriptions.

Naming Consistency3/5

The set is uniformly snake_case and has useful families like polymarket_* and pipeworx_*, plus many clear verb_noun names (list_feeds, read_feed, resolve_entity, validate_claim). However, roughly a third of tools use noun-led or adjective-led names (entity_profile, deep_research, recent_alerts, polymarket_arbitrage), so the pattern is readable but mixed.

Tool Count2/5

34 tools is far beyond what a 'Politics Feeds' server needs, and a large portion of the surface (npm dependency scanning, AI visibility, memory, prediction markets, LLM text generation) is unrelated to the stated purpose. This feels like a kitchen-sink monolith rather than a scoped feed server.

Completeness4/5

Within its actual implied purpose as a broad Pipeworx data-research platform, the lifecycle is well covered: discover, resolve, ask, ground, research, compare, validate, monitor, subscribe, and remember are all present. The gaps are minor—no update-subscription operation and no direct cross-feed search for the feeds named by the server—so agents can work around them.

Resources