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Pkg Go Dev

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds significant behavioral context: it probes each entity internally, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, signal density. 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?

The description is three sentences, each serving a purpose: function statement, process explanation, and use case with output. No wasted words, front-loaded with primary action.

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 parameters (1 required) and no output schema, the description adequately explains the return format (ranked list with score, confidence, signal density). It also clarifies the first entity is treated as the 'subject' for narrative. Annotations cover safety. Sibling tools are listed. The description is complete for this complexity level.

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 descriptions for all 4 parameters. The description does not add extra detail beyond the schema; it mentions 'your brand + N competitors' which is implicit from the entities array. No new parameter semantics are provided, so baseline score of 3 is appropriate.

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, using ai_visibility_check internally, and ranks results. This distinguishes it from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison). The verb 'scan' and resource 'competitor AI presence' are specific and unambiguous.

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 provides a concrete use case: competitive AI-marketing audits, and gives an example question. It implies this tool is for multiple entities, but does not explicitly exclude single-entity usage or mention alternatives. The sibling list includes ai_visibility_check for single probes, which is a natural alternative, but not contrasted.

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

B3.4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all performing similar data lookups. Additionally, many tools focus on prediction markets and arbitrage, which are unrelated to the server's implied Go development domain, causing confusion.

Naming Consistency2/5

Tool names follow inconsistent conventions: some use snake_case (e.g., 'latest_version', 'list_versions'), some use underscores in longer names (e.g., 'ai_visibility_check', 'ask_pipeworx_grounded'), and some use colons in descriptions but not in names. This mix reduces predictability.

Tool Count2/5

With 35 tools, the count is high for a server named 'Pkg Go Dev' that only offers a few Go module-related tools (e.g., get_go_mod, list_versions). The majority are unrelated Pipeworx tools, making the scope mismatched and excessive.

Completeness1/5

The tool set severely lacks coverage for Go development tasks. It only includes basic module lookup tools (version listing, mod retrieval) but misses essential operations like dependency analysis, build commands, or testing. The vast majority of tools cover unrelated domains (prediction markets, company profiles, etc.), leaving the server incomplete for its intended purpose.

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