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Textstats

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds useful context beyond annotations: it states the tool calls ai_visibility_check per entity, ranks by score, and the return format includes score, confidence, and 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 three sentences, front-loaded with the main purpose, then describes the process and output format, and ends with a clear use case. Every sentence earns its place with no redundancy or filler.

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?

The tool is moderately complex (multi-entity, calls sub-tool, ranks results) and has no output schema. The description covers the purpose, process, and return values sufficiently. It relies on schema for parameters and annotations for safety, making it complete enough for an agent to select and invoke.

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 coverage is 100% with parameter descriptions for models, _apiKey, context, and entities. The description reinforces the comparative use case but adds no parameter-level detail beyond the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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, ranks by score, and surfaces most/least recognized. It distinguishes from siblings by naming the underlying sub-tool and the comparative nature.

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, with a concrete example question. It implies use is for multi-entity comparison vs single-entity ai_visibility_check, but does not explicitly name alternatives or exclusions. Clear context, but formal when-not-to-use guidance is missing.

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

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants, deep_research, and bet_research all serve data retrieval with subtle differences. The descriptions help distinguish them, but the sheer number of similar tools creates ambiguity.

Naming Consistency4/5

Tool names are mostly snake_case with a verb_noun pattern (e.g., resolve_entity, validate_claim). A few are nouns like 'readability' or 'text_stats', but the overall style is consistent and readable.

Tool Count2/5

With 33 tools, the server is overstuffed. The server name 'Textstats' suggests a narrow focus, but it covers diverse domains (Polymarket, SEC, memory, subscriptions), making it feel bloated and unfocused.

Completeness3/5

The tool set covers a wide range of data sources and actions, but there are notable gaps for a 'text stats' server—only two tools directly handle text analysis. Additionally, obvious operations like a simple stock quote tool are missing, relying on ask_pipeworx instead.

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