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Nasa Cmr

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.

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, and non-destructive behavior. The description adds context about the ranking, output fields (score, confidence, signal density), and the ordering semantics (first entity as subject). 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 sentences: first states purpose and action, second gives use case and output format. Efficient and front-loaded with key information. No redundant words.

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?

Despite no output schema, the description specifies the return format (ranked list with score, confidence, signal density per entity). All parameters are covered in schema and description. Complexities like probe behavior and ordering are addressed. Complete for the tool's purpose.

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%, so the description's contribution is moderate. It adds meaning by stating that the first entity is treated as the 'subject' for narrative, and clarifies the roles of models, _apiKey, and context 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 clearly states the tool's purpose: comparing AI visibility across multiple entities side-by-side, probing with ai_visibility_check, ranking by score, and returning which is most/least recognized. It distinguishes from sibling ai_visibility_check by focusing on multiple entities and ranking.

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 clear use case (competitive AI-marketing audits) and an example question. It mentions the underlying tool ai_visibility_check, allowing inference of when to use this vs. single-entity probe. However, it does not explicitly state when not to use or list alternative tools.

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

A3.5/5.0
Disambiguation2/5

The tool set mixes NASA-specific tools with a large number of financial and prediction market tools, causing confusion. Within the non-NASA tools, there is significant overlap (e.g., multiple Polymarket analysis tools, several research tools with vague boundaries).

Naming Consistency3/5

Most tools use snake_case, but naming patterns are inconsistent: some start with verbs (search_collections, get_collection), others are noun phrases (entity_profile, deep_research). There is no uniform verb_noun convention across the set.

Tool Count2/5

With 33 tools, the count is high. Although it may be appropriate for the underlying domain (data/prediction markets), it is excessive for the server's stated NASA focus, where only 3 tools are relevant.

Completeness1/5

For a server named 'Nasa Cmr', the tool set is severely incomplete: only three tools cover NASA Earth science (search/granules/collections), lacking any support for missions, datasets, or advanced queries. The non-NASA tools are comprehensive but disconnected from the server name.

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