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String Db

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. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/idempotent hints. The description adds meaningful context: the free default model (Workers AI Llama-3.3-70b), the cost implication of using Anthropic ('you pay Anthropic directly'), and the return structure (per-model score/confidence/signals/raw_response + combined view). 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, front-loaded with the core action, followed by operational details and use cases. Every sentence contributes new information; no filler or repetition.

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?

For a 4-parameter read-only tool with no output schema, the description covers return format, model options, cost behavior, and use cases. It is sufficiently complete for an agent to invoke confidently.

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 extra meaning by explaining that `_apiKey` triggers paid Anthropic calls (pass-through billing) and that the default model is free. This clarifies parameter behavior beyond the schema descriptions.

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 states a specific verb ('Probe'), resource ('one or more LLMs'), and measurable outcome ('score visibility (0-100) per model'), which clearly distinguishes it from sibling tools like scan_competitor_ai_presence. It also names concrete use cases (AI-marketing audits, pre-launch brand checks) that reinforce the purpose.

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 gives explicit use-case context ('Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default vs. optional model selection. It lacks explicit exclusions or comparison to alternatives, but the context is clear enough for selection.

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

B3.2/5.0
Disambiguation2/5

Many tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying data catalog. The six polymarket_* tools also blur together (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread), and resolve vs resolve_entity is an outright collision an agent will likely misselect.

Naming Consistency3/5

There is a solid verb_noun core (list_subscriptions, scan_dependency, validate_claim, suggest_questions, compare_entities) but it is mixed with bare nouns (enrichment, homology, interactions, network) and product-prefixed names (pipeworx_feedback, polymarket_edges, ask_pipeworx). No single consistent pattern holds across the set, though the clusters are internally predictable.

Tool Count2/5

At 36 tools this is well above the 25+ threshold for 'too many,' and the sprawl is not justified by a single coherent domain—prediction markets, bioinformatics, brand visibility, npm scanning, and subscription management are jammed together. The count makes the tool surface hard to navigate even with good descriptions.

Completeness4/5

Within each major cluster the lifecycle feels covered: memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list/recent_alerts), STRING-DB (resolve/homology/interactions/network/enrichment), and Polymarket analysis (scan/edge/arb/fill-risk/track) all form reasonably complete workflows. The main gap is that the server attempts so many domains that none is exhaustively deep, but there are no critical dead ends.

Resources