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Glama

Politics Feeds

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.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds valuable context: it probes each entity via ai_visibility_check, ranks by score, and surfaces most/least recognized. 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.

Conciseness4/5

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

Two sentences, front-loaded with the primary action. Every sentence adds value—no fluff. Could be slightly more terse, but overall efficient and well-structured.

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?

No output schema, but the description explains the output format (ranked list with score, confidence, signal density). Parameters are fully described. The tool's role among siblings is clear. Adequate for a read-only, idempotent tool with rich annotations.

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 adds meaning by noting that the first entity is treated as the 'subject' for narrative purposes, which is not in the schema. This slightly improves parameter understanding.

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 by probing each with ai_visibility_check and ranking results. It distinguishes itself from the sibling ai_visibility_check (single entity) and compare_entities (generic) by specifying the exact verb, resource, and output format.

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?

Provides explicit use case ('competitive AI-marketing audits') and an illustrative question ('does Claude know about us as well as our competitors?'). Lacks explicit when-not-to-use directives but sufficiently implies the tool is for multi-entity comparisons.

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