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Blockstream Info

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds context: returns per-model data with score, confidence, signals, raw_response, and combined view. Explains cost implications for Anthropic calls. 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?

Two well-structured sentences that front-load the core purpose and key details. No redundant or unnecessary information; every sentence earns its place.

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?

Given the tool has 4 well-documented parameters, no output schema, and no nested objects, the description covers all necessary aspects: input, configuration, return format, and use cases. Fully sufficient for correct invocation.

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 description coverage is 100%, but description adds value beyond schema: explains default model, _apiKey purpose (BYO key), and context usage for disambiguation. Enhances understanding of parameter 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 probes LLMs about entities and scores visibility (0-100) per model. It uses specific verbs ('probe', 'score') and identifies the resource (LLMs). It distinguishes itself from siblings like 'scan_competitor_ai_presence' by specifying marketing audits and brand checks.

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 guidance on default model usage and how to use Anthropic with an API key. Implies when to use the tool (marketing audits, brand checks) but does not explicitly state when not to use or list alternatives.

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

B3.1/5.0
Disambiguation1/5

The tool set includes multiple near-identical tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and several overlapping entity/profile tools (entity_profile, compare_entities, resolve_entity, validate_claim) that make it hard for an agent to choose the right one. The Bitcoin-specific tools are isolated and don't integrate well with the rest, creating two separate domains.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ask_pipeworx, fee_estimates), some use underscores (polymarket_edges), and others are standalone verbs (remember, recall, forget). No consistent pattern is followed across the tool set.

Tool Count2/5

With 39 tools, the server is overstuffed for its stated purpose ('Blockstream Info'). The Bitcoin blockchain tools are only 7, while the rest are a sprawling external data platform (Pipeworx) with many redundant tools, making the number feel excessive and unfocused.

Completeness2/5

For a Bitcoin-oriented server, the blockchain tools cover basic operations (address, block, transaction, fee_estimates) but miss essentials like UTXO queries or block details. Meanwhile, the Pipeworx tools dominate and are overcomplete for a server that should be lightweight. The mismatch leaves the Bitcoin part incomplete.

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