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Blockstream Info

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. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare readOnlyHint, idempotentHint, destructiveHint=false. Description adds that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, signal density. No contradiction.

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?

Four sentences, front-loaded with the core purpose, then details. Every sentence adds value without unnecessary words.

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 4 parameters and no output schema, the description covers the output (ranked list with score, confidence, signal density) and the operational logic (probes each entity, ranks). Slightly lacking in exact format, but sufficient.

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 value by noting the first entity is treated as subject, which aids understanding beyond schema.

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 it compares AI visibility across multiple entities side-by-side, ranks by score, and surfaces most/least recognized. It distinguishes from sibling ai_visibility_check (single entity) and compare_entities (generic).

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?

Explicitly states usefulness for competitive AI-marketing audits with an example question. Does not explicitly state when not to use, but the context is clear and alternatives are implied via sibling list.

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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